120 Week 8 F /For WIZARD KIM

Discuss the advantages and disadvantages of Real Estate investing. 

250 word minimum, USE THE ATTACHED REFERENCES AS WELL AS OTHER REFERENCES to answer the questions, cite in APA.    

AuthorAffiliation

Bernard Shusman

January 13, 2017 5:18 PM

– Bernard Shusman (http://www.voanews.com/author/4352.html)

NEW YORK –

America is changing the way it lives. Owning one’s home has always been considered a significant part of the modern American dream – it meant pride and security, control, stability and goodbye to landlords.

It still does, but times change and there are signs that the dream of owning a home may be changing as well.

The ‘bubble’

The Great Recession, which began in late 2007 and lasted for nearly two years, is still having an impact on the U.S. housing market. Since the bottom dropped out a decade ago, “new home starts” – used to describe when construction begins on a new home – have been sluggish.

That was until October, when they hit a nine-year high, then dropped in November, suggesting Americans are confused about where and how they want to live.

Some of the confusion stems from memories of the housing market “bubble” of easy money and credit that burst in December 2007 and was a main cause of the Great Recession. More than 20 million foreclosures were filed during the decade. RealtyTrac Data indicates 7.3 million consumers lost their homes between 2007 and 2014.

“Housing was a great part of the recession,” said housing expert and New York University professor Lawrence White. “Housing prices fell dramatically from their peak in 2006. Nationwide, the average value of a home dropped 30 to 35 percent. We certainly learned in the recession, investing in a house is not a sure-fire way to build wealth.”

However, White says, housing plays a major role in the economic health of the nation.

But for potential homebuyers, it has become a lot harder to get the credit necessary to buy a house. Plus, there’s more paperwork when consumers do qualify.

That leads to a depressed market. Another issue: a lack of inventory, says Pamela Woodward, who owns three real estate offices in New Jersey.

“The political climate, at least for now, has people skittish and nervous about the economy,” she said.

On the plus side, Fannie Mae and Freddie Mac – two government-backed mortgage companies – are raising the amount of money they will guarantee, for the first time since 2006. That will make it easier for buyers to qualify for larger loans, and to get more home for their money.

Diploma vs. mortgage

Lurking in the background of less new construction is the renting vs. buying issue.

Ingo Winzer, CEO of Local Market Monitor, covers 330 local real estate markets across the United States and forecasts home prices in those areas. Winzer says renting is becoming increasingly attractive because many people just don’t have the money to buy.

“There’s not enough income to allow people to get into housing,” he said.

Seattle-based Zillow is an online real estate database and marketplace company that works with Realtors and consumers to evaluate local property markets. Zillow predicts that millennials will eventually be in a financial position to buy, and will boost home ownership. At the same time, Zillow says, renting will become more affordable as incomes rise and growth in rents slows.

One of the biggest reasons that young Americans are waiting to buy their first home is student debt. Millennials, who used to be considered the prime homebuying age, are carrying an estimated $1 trillion in student loan, according to the U.S government.

Case in point: Kevin Clancy, 47, a Realtor in Albany, New York, says his business is good. “But,” he said, “the problem is, the kids are coming out of school with a tremendous amount of student loan debt. It’s like a drag on their finances. I have a girl in my office, she’s 38. She’s still paying $800 a month on student loans. That’s money that could be going toward the mortgage. I think it’s kind of delaying the process.”

Clancy says his view of the under-35 generation is that “they’re buying nice cars, they’re going out to dinner, and they’re staying in apartments. And they’re moving around a lot more with their jobs. They are a lot more mobile.”

Olga Hannout, a ReMax agent in Manalapan, New Jersey, says she’s never seen rentals so high in her 20-plus years in the business. She says she worries the recovery that the United States is enjoying may be artificial, and that’s one reason rentals are high.

A cautious market

Ten years ago, the national percentage of households renting was about 30 percent; today, it’s about 37 percent, according to the public data website departmentofnumbers.com. Each percentage point nationally is about 1 million households.

Rentals are also attractive because renters are spared the worry of potentially plummeting home values.

“We ought to be making rental housing much more respectable, whether in urban or suburban areas,” White said. “Our whole society ought to get away from the fixation of ownership as the only way to go. It’s not the only way to go. Ownership is not for everybody; it takes a certain income, budgetary discipline and a steady income.”

Many Americans learned from the recession that investing in home ownership is not a sure way to build wealth, he says.

Currently, however, home values are on the rise. Zillow, utilizing a survey of more than 100 economic and housing experts, forecasts home values increasing by 3.6 percent this year. Last year saw an increase of close to 5 percent.

America in the middle

Other countries are doing as well as or better than the U.S.

“Internationally, we are sort of in the middle of the pack amongst developed countries,” White said. “In terms of things like percentage of households that own their own homes [and] the interest rate mortgage borrowers pay, the U.S. is in the middle of the pack. We were not the only ones to go through a housing bust.”

Winzer, of Local Market Monitor, keeps track of local real estate markets in the United States.

“The idea used to be that people with a modest education could have fairly well-paid jobs, like 50 years ago, but that’s not the case anymore,” Winzer said. “Jobs that were done by skilled labor are done by skilled robots. I think there are some long-term difficulties. I think the idea that the economy is going to be fantastic again is wishful thinking.”

Currently, employment is at record levels in the U.S. The incoming Trump administration promises more jobs and, if that happens, it is expected that the government and the banks will provide more incentives to stimulate homebuying.

Bouncing back

The American dream may be hard to recapture, but analysts believe many of those who lost homes in the Great Recession may be ready to give it another try.

Realtor Woodward said she saw two pairs of such boomerang, or rebound, buyers this month.

Both couples “strongly believe in the concept of home ownership,” she said.

For them, the American dream lives on.

Word count: 1177

Copyright (c) 2017 Federal Information & News Dispatch, Inc.

Captive Real Estate Investment Trust

By 

WILL KENTON

 Updated Apr 15, 2020

What Is a Captive Real Estate Investment Trust?

A captive real estate investment trust is simply a 

real estate investment trust (REIT)

 with controlling ownership by a single company. A company that owns real estate associated with its business may find it advantageous to bundle the properties into a REIT for the special tax breaks. This tax mitigation strategy can be used by retailers and banks with many stores or branches.

KEY TAKEAWAYS

· A captive REIT is any REIT with greater than 50% ownership stake by a single company.

· Captive REITs are usually subsidiaries.

· As REITs, captive REITs enjoy all of the tax advantages of a standard REIT.

· Comprehensively, captive REIT accounting can be complex for a parent company and the captive REIT subsidiary.

· Accounting and tax professionals should ensure they are fully compliant with all federal and state laws encompassing captive REITs.

Understanding Captive

Real Estate Investment Trusts

A captive real estate investment trust can be created to take advantage of the tax breaks offered by a real estate investment trust (REIT). Companies may choose to develop or take controlling ownership in a REIT for captive status. Controlling or captive status is defined as more than 50% of the voting ownership stake of a REIT.

Companies that build a captive REIT to manage their own real estate properties will typically characterize them as either rental or mortgage REITs. Mortgage REITs (mREITs) provide mortgage capital for the promise of reciprocal income, which is often the basis for a REIT’s revenue. Companies may also use captive real estate investment trusts by transferring real estate into a REIT, and then renting the properties from those REITs.

Real Estate Investment Trusts

A captive REIT is a REIT with controlling ownership from a single company. Beyond that, captive REITs are simply REITs. An entity can be classified as a REIT if it meets certain requirements of the Internal Revenue Service and Title 26 of the Internal Revenue Code. REITs can be trusts, associations, or corporations—but regardless they must all elect to be taxed as corporations.

The Internal Revenue Code allows all REITs to distribute all of their income to their 

shareholders

. This makes REITs similar to partnerships under the tax code since partnerships generally have no income and distribute all of their income through a 

K-1

.

REITs must meet several requirements to qualify for the income distribution 

tax deductions

 that characterize REITs in general. Specifically, a company must meet the following requirements t qualify as a REIT:

· Taxable as a corporation

· Pay at least 90% of taxable income in the form of shareholder dividends each year

· Derive at least 75% of gross income from rents, interest on mortgages that finance real property, or real estate sales

· Invest at least 75% of total assets in real estate, cash, or U.S. Treasuries

· Have at least 100 shareholders (controlling companies may name executives as shareholders in order to meet this requirement)

If an entity meets the REIT requirements, it must pay at least 90% of its income to shareholders and is therefore allowed to take the income distribution as a deduction. Any remaining balance after the required distribution is taxed at the necessary corporate tax rate.

Subsidiary Accounting

Captive REITs are considered subsidiaries and therefore their ownership must be accounted for in some way on the parent company’s financials. Generally, there are three ways to account for subsidiaries and subsidiary ownership on a parent company’s 

financial statements

. Companies can report consolidated financial statements, or they may account for the ownership through either the equity method or the cost method.

Under 

Generally Accepted Accounting Principles

 (GAAP), companies have the option to create consolidated financial statements that integrate all aspects of a subsidiary’s financials if the parent company owns greater than 50% of the ownership rights. Typically, it is not beneficial or applicable for a parent company to include a captive REIT in consolidated financial statement reporting. That’s because of the tax benefits the captive REIT gets on its own, which are often the reason for creating it. Therefore, captive REIT ownership is typically accounted for on a parent company’s financials through either the equity method or the cost method.

Captive REIT Tax Benefits

There can be several tax benefits associated with captive REIT taxes. Federal taxation of REITs is discussed in Internal Revenue Code Title 26, but states may also have their own tax rules for REITs that can increase or decrease the tax benefits.1

In general, the parent company of a captive REIT can deduct rent or mortgage payment costs it pays to its captive REIT, which reduces its 

taxable income

. This is not necessarily a huge benefit because it would typically deduct these expenses anyway. Still, it can create some helpful advantages in payment processing, etc. One of the biggest advantages is that the parent company receives a part of the dividend distribution from the captive REIT, which can potentially be taxed at a lower rate.

The captive REIT enjoys all of the tax benefits of REIT status. It can deduct the 90% or greater amount of its income it distributes to shareholders. It also pays the federal corporation tax rate on any remaining income.

Laws Governing Captive REITs

Because captive REIT subsidiaries can potentially create several advantages, there are some federal and state provisions that target them. In general, most legislation defines captive as controlling ownership of 50%. Federal laws require that any treatments are fair and in line with property valuations and arm’s length negotiations.

Some states have their own special requirements. In some cases, there are limitations that may eliminate 

tax avoidance

 tactics comprehensively. Overall, accounting and tax professionals should ensure that captive REITs and captive REIT accounting are compliant with all federal and state laws. 

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Chapter 86: Real estate mathematics 567

After reading this chapter, you will be able to:

• understand the fundamental real estate math concepts a real
estate agent may be tested on during the state licensing exam and
exposed to during their first four years of practice; and

• calculate common real estate math

problems

, including fee
percentages, area calculation, loan and interest payments and
general computations for real estate investments.

Learning
Objectives

Real estate
mathematics

Chapter

86

amortization table

area

capitalization rate (cap
rate)

financial calculator

net operating income
(NOI)

As a requisite for entry into the real estate profession, a thorough
understanding of the formulas commonly used in the real estate industry is
necessary.

Once the basic formulas and logic behind them are mastered, most
math issues in mortgages, income property, property management and
investment are solved with a basic, nonprogrammable calculator.
Further, a nonprogrammable calculator is provided during the agent and
broker California Bureau of Real Estate (CalBRE) licensing examination.
The examinee is well advised to use the calculator supplied to ensure their
accuracy on the exam.

Once an individual is licensed, a financial calculator is a wise investment
in the real estate industry. Frequently used formulas are pre-programmed
into the financial calculator, such as amortization schedules, making it an
invaluable asset.

The
mathematical
tools you
need

financial calculator
An electronic
calculator
preprogrammed to
perform advanced
financial functions
needed in real estate
transactions.

Key Terms

568 Real Estate Principles, Second Edition

A grasp of mathematical basics is helpful when dealing with:

• percentages;

• fractions;

• area;

• loans;

• investments/cost analysis;

• capitalization (cap) rates; and

• all other mathematical concepts common to real estate transactions.

A percentage needs to be converted to a decimal before any mathematical
computations can be completed. This is a conversion financial calculators are
preprogrammed to make.

When converting percentages to decimals, the basic rule to follow is to move
the decimal point two spaces to the left.

Examples:

50% = 0.5

3% = 0.03

115% = 1.15

Math basics

Percentages

Before delving into problems involving land descriptions and areas, here is a review of
basic units of land measurement:

Township = 36 square miles broken up into 36 sections;

Section = 640 acres and 1 square mile;

Half section = 320 acres, a quarter section = 160 acres, and a quarter of a quarter
= 40 acres;

Acre = 43,560 sq. ft;

Mile = 5,280 ft;

Square mile = 27,878,400 sq. ft (5,280 ft x 5,280 ft) or 640 acres (27,878,400 sq.
ft / 43,560 sq ft);

Square acre = approximately 209 ft x 209 ft;

1 yard = 3 ft;

1 square yard = 9 sq. ft (3 ft x 3 ft);

1 mile = 320 rods; 5,280 ft; and

1 rod = 16.5 ft or 5.5 yards (16.5 ft x 3 ft).

Basic units of
measurements
in real estate

Chapter 86: Real estate mathematics 569

Alternatively, to convert a decimal to a percentage, move the decimal point
two spaces to the right.

Examples:

1.43 = 143%

0.03 = 3%

0.5 = 50%

As the use of a calculator is allowed while taking the CalBRE state licensing
exam, it is beneficial to convert all fractions to decimals and then use the
calculator to complete the computations.

To understand fractions, it is helpful to understand their basic mechanics. A
fraction is composed of a numerator and a denominator. The number on
the top of the fraction is the numerator and the number on the bottom is the
denominator.

When converting a fraction into a decimal, divide the numerator (the number
on top) by the denominator (the number on the bottom).

Examples:

4/5 = 0.8

1/2 = 0.5

3/4 = 0.75

3/100 = 0.03

667/100 = 6.67

The formula for an area of a rectangle is most often used in land measurement.
The area of a rectangle equals length multiplied by width, the result being
the number of square feet within the parcel.

A = L x W

Problem 1

A rectangular lot is 1,230 ft by 2,340 ft. What is the area of the lot in acres?
(Round acres to the nearest whole acre.)

1. Area = 1,230’ x 2,340’ (2,878,200 sq. ft).

2. Conversion of square feet to acres. (One acre equals 43,560 sq. ft.)
2,878,200 / 43,560 = 66 acres.

Solution: 66 acres.

Fractions
converted

Basic
formulas for
area

area
The amount of space
within the boundaries
of a parcel of real
estate.

570 Real Estate Principles, Second Edition

Problem 2

A rectangular lot is comprised of 10 acres. If the length of the lot is 500 feet,
what is its width?

1. 10 acres = 10 x 43,560 sq. ft (435,600 sq. ft).

2. 435,600 sq. ft / 500 ft = 871.2 ft.

Solution: 871.2 ft.

Problem 3

A rectangular lot is comprised of a quarter of a quarter (1/16) of a section of
a township. The width of the lot is 800 feet. What is the length of the lot in
yards?

3. A quarter of a quarter of a section is equal to 40 acres. (One section of a
township equals 640 acres and 1/16 of a section (1/4 x 1/4) is 40 acres.)

4. 40 acres = 40 x 43,560 sq. ft (1,742,400 sq. ft).

5. 1,742,400 sq. ft / 800 ft = 2,178 ft.

6. 2,178 ft / 3 = 726 yards.

Solution: 726 yards.

Problem 4

A rectangular lot measures 1,652 ft by 2,430 ft. The cost per acre for the lot
is $120. How much would the lot cost to purchase? (Round to the nearest
whole acre.)

1. The lot area = 1,652 ft x 2,430 ft. (4,014,360 sq. ft).

2. 4,014,360 sq. ft = 92 acres (4,014,360 / 43,560).

3. 92 acres x $120 = $11,040.

Solution: $11,040.

Another critical formula is the area of a triangle. This formula is used when
determining the area of a triangular shaped lot.

The area of a triangle equals its base multiplied by its height divided by two.

A = (B x H) / 2

Problem 1

A triangular lot features a 200 ft base and a height of 150 ft. How many square
feet are contained in the triangular lot?

1. The lot area = (200 ft x 150 ft) / 2.

2. 30,000 ft / 2 = 15,000 sq. ft.

Solution: 15,000 sq. ft.

Area of a
triangle

Chapter 86: Real estate mathematics 571

The percentage formula is a basic calculation used in real estate mathematics.
It is typically used to determine the amount of a broker’s fee on a transaction.

As compensation for services, a broker is entitled to a broker’s fee typically
stated as a percentage of the:

• sales price;

• loan amount; or

• total rents.

The percentage formula is easily converted to suit a variety of situations.
Usually, two of the three variables will be known, leaving the third to be
determined.

Understanding the basic formula makes solving problems a question of
mechanics.

The percentage formula is as follows:

• Fee = Percent (%) x Principal

Percent (%) = the rate charged.

Principal (P) = the dollar amount of the price, loan or rents.

Given the basic formula and two of the three variables, solutions are
determined as follows:

• To determine the fee, multiply the principal by the rate. P x %

• To determine the rate, divide the fee by the principal. Fee / P

• To determine the principal, divide the fee by the rate. Fee / %

Problem 1

A broker earns a 3% fee on the sale of a $100,000 home. How much will the
broker earn?

Consider what we know:

% = 3%

P = $100,000

Using the percentage formula: Fee = % x P

1. Fee = 3% x $100,000

2. Fee = 0.03 x $100,000

3. Fee = $3,000

Thus, the broker earns a fee of $3,000.

Solution: $3,000

Percentage
formula

572 Real Estate Principles, Second Edition

Problem 2

An agent shares in the broker’s fee based on 40% of the 3% fee the broker is
paid on a transaction. The home sells for $245,000.

An irregular lot is neither an even square, rectangle or triangle. Instead, it is
mathematically broken up into smaller known shapes. Thus, its area is determined by a
combination of the above formulas and adding of the results

First, divide the irregular shaped lot into even squares, rectangles and triangles.
Compute the area of each shape individually then add them to equal the total area of
the lot.

The square portion is 50 ft x 50 ft.

The lot area of the square = (50 ft x 50 ft); thus, 2,500 sq ft.

The rectangular portion is 30 ft x 25 ft.

Editor’s note – Length = 50 ft (the length of the square) less 20 ft. Thus, 30 ft.

Width = 75 ft (the width of the square and rectangle combined) less 50 ft (the
length of the square). Thus, 25 ft.

The lot area of the rectangle = (30 ft x 25 ft); thus, 750 sq ft.

The triangular portion is (40 ft x 30 ft) / 2.

Editor’s note – Width = 65 ft (the width of the rectangle plus the bottom outer
edge of the triangle) less 25 (the width of the rectangle). Thus, 40 ft.

The lot area of the triangle = 1,200 ft / 2; thus, 600 sq ft.

Lastly, combine the lot area of each shape.

2,500 ft (the square) + 750 ft (the rectangle) + 600 ft (the triangle) = 3,850 sq ft.

Solution: 3,850 sq ft.

75’

50’

50’

20’
65’

Figure 1

Area of an
irregular lot

Chapter 86: Real estate mathematics 573

Editor’s note — Alternatively, the purchase price is analogous to the total
loan amount in a mortgage loan brokerage situation, or the total amount of
all rents due during the initial term of a lease agreement in a leasing agent
situation.

How much will the agent earn?

Start with what we know to determine the broker’s fee:

% = 3%

P = $245,000

Using the percentage formula:

1. Fee = 3% x $245,000

2. Fee = 0.03 x $245,000

3. Fee = $7,350

Thus, the broker will earn $7,350. The sales person will then receive 40% of
$7,350.

Using the percentage formula:

1. Fee = 40% x $7,350

2. Fee = 0.4 x $7,350

3. Fee = $2,940

The agent receives $2,940 on the close of the transaction.

Solution: $2,940

Problem 3

A real estate transaction involves both a seller’s broker and a buyer’s broker.
The brokers agree to split a 6% fee paid by the seller 50-50.

You are the buyer’s broker’s agent and will receive 70% of the fee your broker
receives.

If the home sells for $149,500, how much will you receive?

First, compute the total fee to be received by both brokers.

1. Total fee = 6% x $149,500

2. Total fee = 0.06 x $149,500

3. Total fee = $8,970

Thus, the total brokerage fee is $8,970.

Second, calculate the seller’s broker’s fee:

1. Seller’s broker’s fee = 50% x $8,970

2. Seller’s broker’s fee = 0.5 x $8,970

574 Real Estate Principles, Second Edition

3. Seller’s broker’s fee = $4,485

$4,485 is the amount the seller’s broker will receive on the close of the
transaction.

Finally, calculate your share of the fee on in the deal:

1. Your fee = 70% x $4,485

2. Your fee = 0.7 x $4,485

3. Your fee = $3,139.50

You will receive $3,139.50 from your broker on the completion of the sale.

Solution: $3,139.50

Problem 4

A broker lists an office building for sale. The listing agreement calls for a
graduated fee payment computation. The broker agrees to accept a fee of 5%
on the first $150,000 and a smaller percent on the remaining sales price.

The broker sells the office building for $240,000 and earns a total fee of $11,100.

What is the percent of the sales price the broker has agreed they are to be paid
on amounts over $150,000?

The broker earns 5% on the first $150,000 and an unknown percentage on the
remaining $90,000 ($240,000 sales price – $150,000). The total fee received is
$11,100.

Using the percentage formula (Fee = % x P), determine the fee on the first
$150,000.

1. $11,100 = (5% x $150,000) + (?% x $90,000)

2. $11,100 = (0.05 x $150,000) + (?% x $90,000)

3. $11,100 = $7,500 + (?% x $90,000)

Next, determine the percentage the fee amount is of the remainder of the
price.

1. $11,100 – $7,500 = ?% x $90,0000

2. $3,600 = ?% x $90,000

3. $3,600 / $90,000 = 0.04

Convert the amount to a percent by moving the decimal two spaces to the
right. Therefore, the fee is 4% on the amount over $150,000.

Solution: 4%

The percentage formula can also be used in loan calculation problems.

Cost of using the lender’s money = Cost
Loan

problems

Chapter 86: Real estate mathematics 575

Interest rate = Percent

Principal amount of the loan = Principal

When calculating problems involving simple interest, the interest rate is the
rate of interest over one year.

Interest can be further broken down into months by dividing the annual
interest rate by 12, and into days by dividing the interest rate by 360 (months
are uniformly considered to be 30 days to avoid awkward numbers).

Financial calculators and amortization tables make interest calculations
easier. An amortization table is a schedule of monthly loan payments which
show the amount of principal and the amount of interest which comprises
each constant payment until it is paid in full by the end of the term.

Early in the amortization schedule, a majority of the monthly payment is
applied to interest. However, towards the end of the schedule, most of the
monthly payment is applied towards the diminishing principal balance.

In loan problems, time is an important factor and is part of the percentage
formula. Thus, the percentage formula is modified slightly for loan problems
as follows:

Cost of borrowing money (C) = Interest Rate (%) x Time (T) x Principal
(P)

C = % x T x P

When dealing with interest rates for different periods, remember the
following:

• for annual interest, the interest rate is the percent given;

• for monthly interest, the interest rate is equal to the interest rate given
divided by 12 (for the 12 months in the year); and

• for daily interest, the interest rate is equal to the interest rate given
divided by 360.

Problem 1

A borrower owes $15,000 on a straight note payable at the end of the quarter.
The borrower pays $225 in total interest costs with the timely payoff of the
principal. What is the interest rate on the loan?

Editor’s note – A quarter is equal to 3 months.

Using the modified percentage formula and the information given above:

1. $225 = (?%/12 months) x 3 months x $15,000

2. $225 = ?%/4 (one quarter) x $15,000

3. $225 x 4/$15,000 = ?%

4. .06 = ?%

Thus, the interest rate on the loan is 6%.

amortization table
A tabular schedule
detailing the
apportionment of
principal and interest
on each periodic
payment due on an
amortizing loan.

576 Real Estate Principles, Second Edition

Solution: 6%

Problem 2

If an investor earns $360 on a straight note with an interest rate of 8% payable
in 60 days, what is the principal amount of the loan?

C = % x T x P

1. $360 = 8% x 60 days x P

2. $360 = .08 x (60 days/360) x P

3. $360/0.08 x 360/60 = P

4. 4,500 x 6 = P

5. $27,000 = P

The principal amount of the loan is $27,000.

Solution: $27,000

Problem 3

Consider a $200,000 interest-only straight note with monthly interest
payments with a 6% annual interest rate due in 15 years. What is the amount
of the final/balloon payment due on the end of the loan term?

Editor’s note — Some loan problems don’t require the computation of time,
and thus this variable is omitted from the equation.

C = % x P

C = 6% x 200,000

The final/balloon payment equals the total loan balance, plus the last
monthly interest payment. Start by determining the total annual interest
charged for the loan.

1. C = 6% x $200,000

2. C = .06 x $200,000

3. C = $12,000

Next, determine the monthly interest and add this amount to the loan
balance.

4. $12,000/12 months = $1,000

5. $1,000 + $200,000 = $201,000.

The final/balloon payment is $201,000.

Solution: $201,000

Calculations in income property transactions determine the profit from a
sale or the amount an investor will pay for the net operating income (NOI)
produced by the property.

Investment and
cost problems

Chapter 86: Real estate mathematics 577

Problem 1

A seller wants an 8% return (profit) on a $125,000 investment they have in an
unencumbered property they own.

What is the minimum net selling price the seller needs to receive to realize
the 8% return?

First, consider that at $125,000, the seller has a 100% return of their investment.
Thus, the seller needs to realize a 108% total return to receive an 8% profit on
their investment.

Using the percentage formula and the information given:

1. Net price = 108% x $125,000

2. Net price = 1.08 x $125,000

3. Net price = $135,000

For an 8% profit on the original investment, the seller needs to net $135,000
on the sale.

Solution: $135,000

Problem 2

A lender purchases a straight note at an 18% discount. The note has a
remaining principal balance of $60,000 and a 10% interest rate.

What is the lender’s annual yield on its investment in the straight note?

First, compute the amount the lender paid to purchase the principal
remaining due on the note:

1. $60,000 x 18% = the amount of the discount.

2. $60,000 x 0.18 = $10,800

The principal amount of the note minus the amount of the discount equals
the investment the lender has in the note:

3. $60,000 – $10,800 = $49,200

Next, determine the dollar amount of the annual interest accruing on the
$60,000 principal remaining due on the note.

4. Annual interest = 10% x $60,000

5. Annual interest = 0.10 x $60,000

6. Annual interest = $6,000

Lastly, divide the dollar amount of the annual interest ($6,000) accruing on
the note’s principal by the amount invested in the note, i.e., the purchase
price ($49,200):

7. $6,000 / $49,200 = 0.1219

8. 0.1219 = ?%

578 Real Estate Principles, Second Edition

Thus, the lender realized a 12.19% rate of return on the investment in the
straight note.

Solution: 12.19%

Problem 3

A subdivider purchases three lots for $150,000. The three lots are subdivided
into nine parcels. The parcels are all sold for $25,000 each.

What is the subdivider’s return on their investment?

First, calculate the total amount received for the nine parcels:

1. (9 x $25,000) = $225,000

Next, subtract the owner’s cost:

2. $225,000 – $150,000 = $75,000

Thus, $75,000 is the amount the subdivider profited on their investment.

Next, use the percentage formula to determine the percentage return on the
subdivider’s investment:

3. $75,000 = ?% x $150,000

4. $75,000/$150,000 = ?%

5. 0.50 = ?%

Thus, the percentage return on the subdivider’s investment is 50%.

Solution: 50%

Problem 4

An owner sells their property for $230,000. The owner takes a profit of 15%
over the amount they originally invested in the property. How much has the
owner invested in the property?

The way to conceptualize the problem is as follows: the owner will receive a
100% return of their investment in the property, plus an additional return on
the investment of 15%. Thus, the owner’s total return will equal 115%.

Use the percentage formula to determine how much the owner originally
paid for the property:

1. $230,000 = 115% x P

2. $230,000 = 1.15 x P

3. $230,000/1.15 = P

4. $200,000 = P

Thus, the investor originally paid $200,000 for the property.

Solution: $200,000

Chapter 86: Real estate mathematics 579

Problem 5

An investor purchased property listed at $100,000. The investor paid 15% less
than the property’s listed price. The investor then resold the property at the
prior owner’s listed price.

What is the investor’s percentage return?

First, determine the price the investor paid for the property. If the investor
received a 15% discount on the listed price, the investor paid 85% (100% –
15%) of $100,000, the listed price.

1. Price paid = 85% x $100,000

2. Price paid = 0.85 x $100,000

3. Price paid = $85,000

Since the investor sold the property for $100,000, the investor made a $15,000
profit on the sale.

Using the percentage formula:

4. $15,000 = ?% x $85,000

5. $15,000/$85,000 = ?%

6. 0.1765 = ?%

The investor received a 17.65% return on the purchase and sale of the
investment property.

Solution: 17.65%

Problem 6

A seller wishes to net a 14% profit over the original $240,000 purchase price
of real estate.

What sales price does the property need to receive to yield a 14% return over
the original price after the broker is paid a 6% fee out of the net proceeds?
(Round answer to nearest whole number.)

Using the percentage formula, determine the amount of net proceeds the
seller needs to receive to realize a gain of 14% over the original purchase
price. The seller’s net proceeds need to be 114% of their original investment.

1. Net proceeds = 114% x $240,000

2. Net proceeds = 1.14 x $240,000

3. Net proceeds = $273,600

The net proceeds from the sale of the property to yield a 14% return would be
$273,600.

To determine the sales price after paying the 6% broker’s fee, consider that
$273,600 equals 94% (100% – 6%) of the total sales proceeds needed to net 14%
after the 6% fee.

580 Real Estate Principles, Second Edition

Using the percentage formula:

4. $273,600 = 94% x P

5. $273,600/0.94 = P

6. $291,063.83 = P

Rounding to the nearest whole number, the price needed on a sale to yield
14% over the original purchase price after paying a 6% broker’s fee is $291,064.

Solution: $291,064

Problem 7

A borrower took out a $300,000 loan which contained a 3% prepayment
penalty. The borrower paid two points to the lender to buy down the interest
rate to 4.5%. The borrower sold the property and paid off the mortgage after
seven years.

If the loan had an average balance of $250,000 during the seven years the
borrower owned the property, what was the lender’s total gross profit on the
loan?

Editor’s note — Solve this problem by individually calculating the lender’s
profit on each component of the loan and totaling these amounts.

1. Total paid for points = $300,000 x 2% = $300,000 x 0.02 = $6,000

2. Total prepayment penalty = $300,000 x 3% = $300,000 x. 0.03 = $9,000

3. Total interest paid per year = $250,000 x 4.5% = $11,250

4. Total interest paid when loan was paid off = $11,250 x 7 years = $78,750

5. $6,000 + $9,000 + $78,750 = $93,750.

The lender’s total gross profit on the loan $93,750.

Solution: $93,750.

With income producing property, the value of the property is determined by
capitalizing the net operating income (NOI) generated by the property
(rental income minus operating expenses).

The rate of return an investor expects on their investment after rental operating
expenses are subtracted from rental income is called the capitalization rate
(cap rate).

Returning to the percentage formula:

Net operating income or loss = NOI

Capitalization rate = %

Purchase price = P

Capitalization
rate

capitalization rate
(cap rate)
The annual rate of
return on investment
produced by the
operations of an
income property or
sought by an investor
on the investment of
capital. The cap rate is
calculated by dividing
the net operating
income by the price
asked or offered for
income property.

Chapter 86: Real estate mathematics 581

Problem 1

An apartment building produces an NOI of $24,000. A buyer seeks an annual
rate of return of 12%.

What price does the buyer pay for the property?

Using the percentage formula:

1. $24,000 = 12% x P

2. $24,000/0.12 = P

3. $200,000 = P

Thus, the buyer is to pay no more than $200,000 to yield a 12% rate of return.

Solution: $200,000

Problem 2

Rent on each unit in a four-unit apartment building is $545 per month. The
owner has been receiving a rate of return equal to an 8% cap rate on their
investment in the property.

If the rent for each unit drops to $500 per month, what is the owner’s loss in
value over the year?

First, determine the rental loss for all units over the year.

1. $545 – $500 = $45 loss per unit per month

2. $45 x 12 = $540 loss per year per unit

3. $540 x 4 = $2,160 loss for the entire complex

Using the percentage formula:

4. $2,160 = 8% x P

5. $2,160/0.08 = P

6. $27,000 = P

Thus, the owner suffers a $27,000 loss in value over the year.

Solution: $27,000

Editor’s note – This can also be calculated by subtracting the total annual rent
at $500 per month from the total annual rent at $545 per month. Problem 3
below illustrates this alternative method of calculation using a different set
of facts.

Problem 3

An owner owns a 5-unit apartment building. The owner uses a cap rate of
10% on their investment. The owner usually realizes an NOI of $650 per
month per unit. However, due to an increase in rents, the owner now nets
$700 per month per unit.

What is the corresponding increase in value for the apartment building?

net operating
income (NOI)
The net revenue
generated by an
income producing
property as the
return on capital,
calculated as the sum
of a property’s gross
operating income
less the property’s
operating expenses.
[See RPI Form 352 §4]

582 Real Estate Principles, Second Edition

First, compute the value of the apartment building when the net income
was $650 per unit.

The total net income over the year for the apartment building is $39,000
($650 x 5 units x 12 months).

Using the percentage formula:

1. $39,000 = 10% x P

2. $39,000/0.1 = P

3. $390,000 = P

Next, compute the value of the apartment building at the new net income.

The new total net income over the year for the apartment building is $42,000
($700 x 5 units x 12 months).

Using the percentage formula:

4. $42,000 = 10% x P

5. $42,000/0.1 = P

6. $420,000 = P

Thus, the increase in value for the apartment building is $30,000 ($420,000 –
$390,000).

Solution: $30,000

Chapter 86: Real estate mathematics 583

As a requisite for entry into the real estate profession, a thorough
understanding of the formulas commonly used in the real estate
industry is necessary.

The formula for an area of a rectangle is most often used in land
measurement. The area of a rectangle equals length multiplied by
width.

A = L x W

Another critical formula is the area of a triangle. This formula is used
when determining the area of a triangular or irregular shaped lot. The
area of a triangle equals its base multiplied by its height divided by two.

A = (B x H) / 2

The percentage formula is a basic formula typically used to determine
the amount of a broker’s fee on a transaction. The broker’s fee equals the
percentage multiplied by the principal amount.

F = % x P

The percentage formula is also used in loan calculation problems.
However, in loan problems, time is an important factor and is added
to the percentage formula. Thus, the cost of borrowing money equals
the interest rate on the loan, multiplied by the term of the loan (time),
multiplied by the principal amount.

C = % x T x P

amortization table …………………………………………………………… pg. 575
area …………………………………………………………………………………… pg. 569
capitalization rate (cap rate) …………………………………………… pg. 580
financial calculator …………………………………………………………. pg. 567
net operating income (NOI) ……………………………………………. pg. 581

Chapter 86
Summary

Chapter 86
Key Terms

No quiz or exam questions are based on this chapter.

Notes:

House Prices, Home Equity Borrowing, and
Entrepreneurship

Stefano Corradin
European Central Bank

Alexander Popov
European Central Bank

This paper shows that housing wealth helps alleviate credit constraints for potential
entrepreneurs by enabling home owners to extract equity from their property and invest
it in their business. Using a large U.S. individual-level survey dataset for the 1996–2006
period, we find that a 10% increase in home equity raises the share of individuals who
transition into self-employment each year from 1% to 1.07%. Our results persist when
we use proxies for aggregate housing demand shocks and for the topological elasticity of
housing supply to generate variation in home equity that is orthogonal to entrepreneurial
choice. (JEL G21, L26)

In this paper, we identify the effect of housing wealth on new business creation
by exploiting the exogenous variation in home equity induced by the U.S.
housing boom of the late 1990s and early to mid-2000s. Using a uniquely
suited individual-level dataset of U.S. home owners and renters, we investigate
how changes in home equity affect the probability of becoming an entrepreneur.
This allows us to study whether nascent entrepreneurs are deterred by credit
constraints and whether the ability to extract housing wealth by increasing their
mortgage helps them to overcome these constraints.

A number of influential papers have suggested that potential entrepreneurs
can be discouraged from starting a business if borrowing constraints prevent
them from raising sufficient capital.1 Black and Strahan (2002), Cetorelli and

We thank Geert Bekaert, Patrick Bolton, Luigi Guiso, Harry Huizinga, David Hirshleifer (the editor), Dwight
Jaffee, Augustine Landier, Ross Levine, Deborah Lucas, Alexander Michaelides, Virgiliu Midrigan, Marco
Pagano, Jonathan Parker, Maria Fabiana Penas, Monika Piazzesi, Robert Shiller, Ken Singleton, Phil Strahan,
Amir Sufi, Roine Vestman, Gianluca Violante, Nancy Wallace, four anonymous referees, and seminar participants
at the ECB, the Third Entrepreneurial Finance and Innovation Conference in Boston, the 2013 ASSA meeting
in San Diego, Carlos III University of Madrid, the 2013 SED meeting in Seoul, Tilburg University, Cass
Business School, and the 2014 EFA meeting in Lugano for valuable comments. Roberto Felici, Carlos Garcia de
Andoain Hidalgo, and Thomas Kostka provided outstanding research assistance. The opinions expressed herein
are those of the authors and do not necessarily reflect those of the ECB or the Eurosystem. Supplementary
data can be found on The Review of Financial Studies web site. Send correspondence to Alexander Popov,
European Central Bank, Financial Research Division, Sonnemannstrasse 22, D-60314 Frankfurt, Germany.
E-mail: Alexander.Popov@ecb.int.

1 For important contributions, see Evans and Jovanovic (1989), Evans and Leighton (1989), Holtz-Eakin, Joulfain,
and Rosen (1994), Blanchflower and Oswald (1998), and Wang, Wang, and Yang (2012), among others. See Kerr
and Nanda (2009b) for a thorough review of the literature.

© The Author 2015. Published by Oxford University Press on behalf of The Society for Financial Studies.
All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
doi:10.1093/rfs/hhv020 Advance Access publication March 5, 2015

The Review of Financial Studies / v 28 n 8 2015

Strahan (2006), and Kerr and Nanda (2009a) document a strong impact of
positive shocks to access to external finance, following banking deregulation
in the United States, on the rates of new business creation. A house price boom
can provide another source of exogenous shocks to credit constraints whereby
start-ups can extract the additional home equity in their property and invest it
in their business. The effect is potentially significant given that residential
property represents 60% of all personal wealth in the United States (U.S.
Census 2010) and that prior research has already documented the households’
propensity to extract equity from their home, to various ends. For example,
Hurst and Stafford (2004) highlight home equity borrowing as a mechanism
whereby households smooth their consumption over time. Mian and Sufi (2011)
empirically investigate how existing homeowners responded to the rising value
of their home equity between 2002 and 2006. They provide evidence that
this home equity-based borrowing channel, which may have been fueled by
increasing availability of mortgage credit, was an important cause for the rapid
rise in household leverage in the run-up to the financial crisis of 2008–2009.

The magnitude of the U.S. house price boom provides for an ideal
identification of the home equity borrowing channel of entrepreneurship.
Nationally, real home prices rose by 86% between the fourth quarter of 1996 and
the first quarter of 2006 (Shiller 2007), but there were large regional differences.
For example, over this period home prices almost tripled in Miami but declined
by 10% in Detroit. Figure 1 plots the change in establishments births between
2003 and 2006 (the peak of the housing boom) against the change in the
state-level Federal Housing Finance Agency house price indices over the same
period, inflation-adjusted, for the fifty U.S. states, plus the District of Columbia.
A positive relationship is readily available, suggesting higher entrepreneurial
activity in states with a house price boom.

We investigate the link between housing and new business creation using
microdata on home equity and business ownership from the Survey of Income
and Program Participation (SIPP) of the U.S. Census Bureau, from 1997 to
2006. In each survey year, the respondents are asked whether they run and/or
operate a business and the value of their business equity. They are also asked
questions about their residential property. We construct an empirical proxy for
home equity as the difference between the value of the household’s property
and the size of the household’s mortgage. The survey also contains household-
level data on a variety of other relevant characteristics, such as the household’s
nonhousing wealth and labor income and the head’s age, race, education, and
marital status. We control for housing debt, too, to account for the possibility
that more levered households may harbor higher entrepreneurial tendencies.
To identify variation across households residing in the same metropolitan state
area (MSA) at the same point in time, we control for MSA×year fixed effects.
The three SIPP panels put together contain information on 78,793 unique
households, for 1996–2000, 2001–2003, and 2004–2006, for a total of 217,014
possible observations.

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House Prices, Home Equity Borrowing, and Entrepreneurship

Figure 1
Changes in rates of new business creation and changes in house prices
This figure plots the change in establishments births between 2003 and 2006 (the peak of the U.S. housing boom)
against the change in the state-level Federal Housing Finance Agency house price indices over the same period,
for the fifty states, plus the District of Columbia. House price data are inflation adjusted.

The central part of the paper deals with the identification of the collateral
channel. There are nontrivial endogeneity concerns related to the impact of
home equity on the transition into entrepreneurship. Individuals who ultimately
become entrepreneurs can be different from the rest in ways that matter for
entrepreneurship. For example, they may disproportionately reside in areas with
booming local economies, where the propensity to start a business is higher. In
fact, it is highly likely that the areas that experienced the largest house price
booms during the early to mid-2000s were also intrinsically entrepreneurial
areas (such as large coastal cities). Alternatively, an outward shift in the supply
of credit which accelerated house price growth (see Mian and Sufi 2009) may
have also relaxed constraints on business loans, leading to higher levels of
self-employment. And even in the absence of such omitted variable bias, rising
house prices can feed back into local demand booms, raising the return to
entrepreneurship.

We implement a number of empirical strategies to differentiate exogenous
shocks to housing wealth from local economic effects (such as demand booms),

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The Review of Financial Studies / v 28 n 8 2015

which may be correlated with changes in house prices and in small business
creation. First, we employ a version of the identification strategy suggested by
Chetty and Szeidl (2010). Namely, we use the change in average U.S.-wide
house prices between the year when the house was bought and the current year,
divided by the local (MSA-level) topological elasticity of housing supply from
Saiz (2010), as an instrument for home equity. The idea behind this approach
is that an increase in the economy-wide demand for housing will increase
house prices, and this effect should be stronger in MSAs with less elastic
housing supply, where the adjustment in response to aggregate demand shocks
takes place on the price margin. Second, we repeat our analysis only on the
subsample of individuals who live in the MSAs with the most elastic housing
supply. By doing so, we address the concern that the propensity to start a
business can be positively correlated with the local price response to economy-
wide changes in housing demand. Third, we seek to alleviate concerns that the
house price boom is driving entrepreneurship directly, by excluding business
start-ups in construction, finance, and real estate from the analysis. We also
exclude business start-ups in health and education to account for the fact that the
collateral channel we identify may be contaminated by a housing boom-driven
increase in the demand for services with high-income elasticities. Fourth, we
compare homeowners to renters, hypothesizing that the same increase in house
prices should affect entrepreneurial propensity relatively more for homeowners
as renters lack entirely the collateral channel. Finally, we compare the change in
mortgage debt of new business owners and of nonbusiness owners around the
same point in time in the same geographic locality. If the home equity borrowing
channel is active, mortgage debt should increase more for new business owners,
year-on-year, as they are expected to draw down their home equity to increase
their business investment.

We find that the probability of starting a new business is strongly, positively
correlated with the value of the home equity. A 10% increase in home equity
raises the probability that a non-business-owning household will switch to
entrepreneurship in the next period by up to 7%. This effect translates into
an increase in the share of households in the sample who switch to self-
employment from around 1% to 1.07%, from one year to the other. The results
remain statistically robust to model specification and to accounting for a number
of data features and alternative explanations. In particular, the positive effect
of lagged home equity on the probability of starting a business is robust to
controlling for a wide range of demographic and income characteristics, for
the local business cycle, and for the bankruptcy code, and it is not driven by
the propensity of individuals to start a new business when more levered. It is
positive and significant regardless of whether we define business ownership in
terms of owning and operating a business, in terms of holding nonzero business
equity, or in terms of either. It is still present in the data when we correct for
potential incidental variables problem and when we account for the fact that
the SIPP oversamples low-income areas. Crucially, it survives when we use

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House Prices, Home Equity Borrowing, and Entrepreneurship

global housing demand shocks and the local elasticity of housing supply to
extract the exogenous element of the change in housing wealth, when we look
at areas in which the propensity to start a business is unlikely to be correlated
with changes in house prices, when we exclude new businesses likely driven by
local demand booms, and when we compare owners to renters. We also find a
strong positive correlation between new business ownership and the change in
mortgage debt. This implies that once they switch away from fixed income to
entrepreneurship, individuals tend to draw down their home equity to finance
their business investment, confirming that one’s house can serve as efficient
collateral in business financing.

Our empirical results differ from Hurst and Lusardi (2004), who use
microlevel data from the Panel Study on Income Dynamics (PSID) to argue that
liquidity constraints do not matter for entrepreneurship for most of the wealth
distribution. Furthermore, they find that households that lived in regions in
which house prices appreciated strongly were no more likely to start a business
than were households in other regions. Their analysis is mainly conducted on
data from the 1998–1994 period when house prices in the United States were
relatively flat. Conversely, we are able to exploit the effect of the large increase
in house prices during the early to mid-2000s, when in some states (such as
California) house prices doubled over the span of five years.

Our reduced-form estimates imply potentially important linkages between
housing and real economic activity. For example, our results suggest that the
housing boom before the Great Recession may have resulted both in higher
rates of new business creation and in higher investment. This conjecture is
corroborated in aggregate industry-level data by Adelino, Schoar, and Severino
(Forthcoming) who document that areas with a bigger increase in house
prices between 2002 and 2007 experienced a strong increase in small business
employment relative to large business employment. Our findings also inform
the work of Loutskina and Strahan (2015), who find that positive shocks to
local house prices exert a strong positive effect on local economic growth.
Related to our work, Chaney, Sraer, and Thesmar (2012) examine listed U.S.
firms over the 1993–2007 period and provide evidence that when the value of a
firm’s real estate appreciates by $1, its investment increases approximately
by $0.06. The evidence presented in this paper also informs the work of
Robb and Robinson (2014), who examine the capital structure of start-ups
and find that new firms extensively rely on formal credit, with many start-ups
receiving debt financed through the personal balance sheets of the entrepreneur.
Our results also have important potential implications for economic activity
during downturns, suggesting that high levels of mortgage debt after 2006 may
have depressed self-employment, denting the positive effect on new business
creation of higher unemployment rates. Our work is thus related to the evidence
of Midrigan and Philippon (2011), who find that both output and employment
declined more after 2007 in regions in which household leverage increased
more between 2001 and 2007.

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The Review of Financial Studies / v 28 n 8 2015

The paper closest to ours is that by Schmalz, Sraer, and Thesmar (2013),
who compare French homeowners and renters and find that homeowners are
more likely to start a business in areas in which house prices appreciated more
because higher collateral values increase borrowing capacity. Our paper is
different in two important ways. First, we observe the actual housing wealth
that homeowners have in their property. Therefore, we can estimate the effect of
a change in home equity on the probability of transition into entrepreneurship
within the sample of homeowners, in addition to comparing owners and renters,
and so we do not have to deal with the concern that homeowners may harbor
higher latent entrepreneurial tendencies than renters. Second, because Schmalz,
Sraer, and Thesmar (2013) neither observe bank lending nor levels of home
equity, they do not know whether the collateral channel operates by allowing
homeowners to secure a larger business loan against the rising value of their
property, or by enabling them to extract the additional home equity, thus
bypassing the project screening function of the banking sector. Because we
observe changes in mortgage levels, we find that nascent U.S. entrepreneurs
indeed extract housing wealth from their residential property, by increasing their
mortgage debt. Our paper thus directly identifies the home equity borrowing
channel of entrepreneurship.

1. Data

We estimate the effect of changes in housing wealth on the propensity to start
a business using household level survey data from the Survey of Income and
Program Participation (SIPP) of the U.S. Census Bureau from 1996 to 2006.
In each survey year, the respondents are asked questions related to business
ownership. The survey also contains questions on the value of the house and
on the size of the mortgage, allowing us to construct a proxy for home equity
by taking the difference of the two.

The survey contains household-level data on a variety of additional
individual characteristics. In particular, it has a detailed inventory of the
household’s financial assets, in addition to demographic characteristics,
which are theoretically related to entrepreneurial choice and business equity
ownership, such as age, gender, race, education, and marital status. At each
moment, SIPP tracks approximately 30,000 households. During the period
considered, information was collected from three consecutive groups of
households that were interviewed during the years 1996–2000 (four times),
2001–2003 (three times), and 2004–2006 (two times), respectively. The three
SIPP panels put together contain information on 78,793 unique households, for
a total of 217,014 possible observations.2

2 The SIPP oversamples individuals from areas with high poverty concentrations. We perform robustness checks
to make sure that our main results are not affected by this feature of the survey.

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House Prices, Home Equity Borrowing, and Entrepreneurship

During its active period, each panel is interviewed every year, and panels of
households do not overlap across periods. The longitudinal nature of the survey
enables us to analyze dynamic characteristics, such as changes in employment
status and income, changes in household and family composition, or changes
in housing dynamics. The survey’s cross-sectional features allow us to keep
track of housing wealth. We focus on the identification that arises when the
value of the property, and consequently of the home equity, changes.

Theory provides little guidance on how to classify “entrepreneurs” (see
Hurst and Lusardi 2004). The SIPP allows us to distinguish between direct
ownership of business and ownership of business equity, which may or may
not be ownership of equity in the household’s own business. Specifically, we
utilize responses in the survey to the question “Did the household own and
operate a business in the previous year?” to define business ownership, as well
as responses to the question “What is the value in dollars of the household’s
total business equity?” to define ownership of business equity. This strategy is
somewhat richer than previous studies utilizing household data on entrepreneur-
ship. For example, Hurst and Lusardi (2004) define entrepreneurship from a
question in the PSID, which asks households whether they “[…] own a business
[…] or have a financial interest in any business enterprise,” so they are unable
to distinguish between direct and indirect ownership.

In terms of the household’s assets, we calculate net wealth as total wealth
minus total debt. Total debt includes any mortgage on the household’s current
home. We later distinguish between mortgage and nonmortgage debt. Total net
wealth excludes the value of equity in the house.

To examine the role of home equity on the transition into entrepreneurship,
we create a pooled sample of nonbusiness owners from the three survey waves.
A household is defined as entering entrepreneurship if the household head or
the spouse owns and operates a business in the very next period of the same
survey wave. In robustness checks, we define entrepreneurship as ownership
and operation of a business in any of the subsequent periods of the same survey
wave. Consistent with Hurst and Lusardi (2004), we eliminate households in
which the head is still in school or is close to retirement and focus on nonretired
household heads between the ages of 22 and 60.

In addition to household information, we include data on gross state product
(GSP) growth, state unemployment, and a proxy for homestead exemptions.
The economic rationale for including the former two is that a more vibrant
economy and/or a more depressed local labor market can raise the returns to
self-employment (see Fairlie 2013). Regarding the latter, homestead exemption
enables a filer for bankruptcy to retain home equity in her primary residence
up to the exempted amount. A higher exemption may thus increase the rates
of new business creation by imposing a lower cost on a potential business
exit. At the same time, because the debts of the firm are personal liabilities
of the firm’s owner, lending to the firm is legally equivalent to lending to its
owner. Higher homestead exemptions can thus endogenously generate tighter

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Table 1
Variables summary statistics

Variable No. observations Mean SD Min. Max.

Business owner next period 46,679 0.01 0.10 0 1
Age

31–40 58,127 0.29 0.46 0 1
41–50 58,127 0.30 0.42 0 1
51–60 58,127 0.22 0.43 0 1

Education dummy
High school 58,127 0.25 0.43 0 1
Some college 58,127 0.32 0.47 0 1
College or more 58,127 0.25 0.43 0 1

Dummy: African American 58,127 0.14 0.35 0 1
Dummy: Female 58,127 0.51 0.50 0 1
Dummy: Married 58,127 0.53 0.50 0 1
Labor income 58,127 $48,998.01 $52,144.90 $0 $1,212,060
Dummy: Unemployed 58,127 0.03 0.17 0 1
Household nonhousing wealth 54,626 $74,008.71 $1,063,061 $-1,013,580 $22,047,280
Home equity 58,127 $44,111.26 $80,412.79 $-299,446 $546,000
Home mortgage 58,127 $43,548.18 $67,443.40 $0 $330,002
Home property value 58,127 $87,659.44 $122,718.70 $0 $850,000
State unemployment 58,127 5.13 0.94 2.28 7.81
GSP growth 58,127 5.71 2.30 0.08 14.85
Homestead exemption/100,000 58,127 $2.11 $3.60 $0 unlimited

The sample includes all households in SIPP for the 1996–2000, 2001–2003, and 2004–2006 waves between
the age of 22 and 60 that did not own a business the first time they were interviewed. “Business owner next
period” is a dummy variable equal to one if the household owns or operates a business in the next period. “Age
31–40” is a dummy variable equal to one if the household head’s age is between 31 and 40 years. “Age 41–50”
is a dummy variable equal to one if the household head’s age is between 41 and 50 years. “Age 51–60” is a
dummy variable equal to one if the household head’s age is between 51 and 60 years. “High school” is a dummy
variable equal to one if the household head has finished at most high school. “Some college” is a dummy variable
equal to one if the household head is a college dropout. “College or more” is a dummy variable equal to one
if the household head has at least a college degree. “African American” is a dummy variable equal to one if
the household head is African American. “Female” is a dummy variable equal to one if the household head is a
female. “Married” is a dummy variable equal to one if the household head is married. “Labor income” denotes the
annual household income from supplied labor. “Unemployed” is a dummy variable equal to one if the household
head is currently unemployed. “Household nonhousing wealth” denotes the total wealth of the household net of
the amount of home equity. “Home equity” denotes the difference between the value of the household’s property
and the value of the household’s mortgage. “Home mortgage” denotes the value of the household’s mortgage.
“Home property value” denotes the value of the household’s property. “State unemployment” denotes the current
rate of unemployment in the household’s state. “GSP growth” denotes the current rate of gross state product
growth in the household’s state. “Homestead exemption” denotes the property value that can be exempted in
bankruptcy procedures; we have assigned a value of $1,000,000 in states with unlimited homestead exemption.
Omitted category in age is “Age 22–30.” Omitted category in education is “High school dropout.”

financial conditions. For example, Berkowitz and White (2004) show that small
firms located in states with unlimited homestead exemptions are more likely
to be denied credit. In our sample, the homestead exemption ranges from $0 in
Maryland to an unlimited amount in eight U.S. states in 2006.

Table 1 presents summary statistics of the main variables in the data. There are
a total of 58,127 households observed during the first year of each survey wave.
Of the 46,679 households who answered the question “Did the household own
and operate a business in the previous year?,” 453 answered “yes,” implying
that about 1% of nonentrepreneurs become business owners each year. Fifty-
nine percent of household heads are aged between 31 and 50 years; 57% have
at least some college education; 14% are African American; 51% are female;
53% are married; and 3% are unemployed. In terms of financials, average

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Table 2
Descriptive statistics of new business owners and nonbusiness owners

Variable Nonbusiness Business p-value
owner next period owner next period of difference

Age
31–40 0.288 0.322 0.07
41–50 0.303 0.325 0.03
51–60 0.234 0.216 0.08

Education dummy
High school 0.254 0.210 <0.01 Some college 0.325 0.318 0.50 College or more 0.255 0.358 <0.01

Dummy: African American 0.137 0.064 <0.01 Dummy: Female 0.517 0.444 <0.01 Dummy: Married 0.539 0.673 <0.01 Labor income $49,368 $64,928 <0.01 Dummy: Unemployed 0.027 0.031 0.17 Household nonhousing wealth $76,455 $160,363 0.02 Home equity $45,736 $78,232 <0.01 Home mortgage $45,064 $76,567 <0.01 Home property value $90,800 $154,798 <0.01 State unemployment 0.051 0.052 0.34 GSP growth 0.057 0.059 0.40 Homestead exemption/100,000 $2.106 $2.378 0.01

The sample includes all households in SIPP for the 1996–2000, 2001–2003, and 2004–2006 waves between the
age of 22 and 60 that did not own a business the first time they were interviewed. All statistics are means. For
variable definitions, see Table 1. The unweighted percentage of households that became business owners in the
next period after they were interviewed for the first time is 0.010.

labor income is around $49,000, and average nonhousing wealth (i.e., total
wealth excluding home equity) is around $74,000. The average property is
worth $87,659, with roughly equal size mortgage and home equity. Finally,
average state-level unemployment over the period was 5.1%; average GSP
growth was 5.7%; and on average $211,000 worth of home property value
could be exempted from bankruptcy procedures.3

Table 2 reports descriptive statistics on the subsample of household that
transitioned into entrepreneurship in the very next period. We compare
those to descriptive statistics on the subsample of households that remained
nonbusiness owners. In most cases, the differences between the two samples
are statistically significant, and in some cases, the differences are economically
large. For example, those transitioning into entrepreneurship are on average
more educated and are more likely to be white, male, and married. They are
also more likely to have a higher labor income and have higher nonhousing
wealth. Importantly, those transitioning into entrepreneurship have more home
equity, as well as a more valuable property and higher mortgage debt. This
implies that when estimating the effect of home equity on the transition into
entrepreneurship, we need to pay attention to differences in mortgage debt
as otherwise our estimates can be contaminated by the independent effect of
leverage on the probability of starting a business. Finally, Table 2 implies

3 We have assigned a value of $1,000,000 to the home exemption in states in which the homestead exemption is
unlimited.

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Table 3
Characterizing new business owners

Variable Mean

Business equity new business owners $27,874.97
Business equity nonbusiness owners $3,378.97
Industry
Agriculture, forestry, fishing, and hunting 0.023
Mining 0.000
Construction 0.155
Manufacturing 0.031
Wholesale trade 0.008
Retail trade 0.085
Transportation, warehousing, and utilities 0.070
Information 0.008
Finance, insurance, real estate, and rental and leasing 0.047
Professional, scientific, management, administrative, and waste management 0.264
Education, health, and social services 0.109
Arts, entertainment, recreation, accommodation, and food services 0.124
Other services (except public administration) 0.070
Public administration 0.008
Active military duty 0.000

The sample includes all households in SIPP for the 1996–2000, 2001–2003, and 2004–2006 waves between the
age of 22 and 60. The share of each industry is determined based on the new business owners who reported the
NAICS code of their business.

that individuals are more likely to transition into entrepreneurship if they
live in a state with (marginally) lower unemployment and a higher homestead
exemption.4

Table 3 summarizes the data on new business owners, that is, on individuals
who transitioned into ownership and operation of a business between the
previous and the current period. The average business equity of new business
owners is $27,875. Although it is considerably higher than the average
business equity of nonbusiness owners ($3,379), its relatively low level
implies that entrepreneurs in the sample are on average not owners of
large businesses or important job creators. This is confirmed by the sectoral
breakup of new businesses reported in the table: most of the new business
owners are in professional services, followed by sectors such as construction;
arts, entertainment, recreation, accommodation, and food services; education,
health, and social services; and retail trade. Only 3.1% of the new entrepreneurs
are in manufacturing. These stylized facts caution against deriving implications
in terms of job creation from observations on transition into self-employment.

2. Empirical Methodology and Identification

The main hypothesis that we test is that an increase in house prices will increase
the probability that an individual will transition into entrepreneurship. The
intuition for this result is that if household leverage remains the same (i.e.,
keeping the mortgage balance constant), a higher value of the property will

4 All of these state-level variables are time varying.

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increase the home equity, raising the value of potential business investment
and reducing the effective cost to the agent of switching from fixed income to
variable income.5

We test this prediction using the following nonlinear probability model:

Prob(new businessij t +1 = 1|Zij t ) = ϕ(Zij t ) = ϕ
(
β1 + β2 ·ln(1 + home equityij t )

+β3 ·Xij t + β4 ·�j t + εij t
)
, (1)

where “new business” is a dummy equal to one if household i in MSA j does
not own or operate a business during the period when first interviewed (t )
but does so in the next period (t + 1). “home equityij t ” denotes the difference
between the value of the household’s property and the size of the household’s
mortgage debt at time t . Because the main explanatory variable is defined as
“ln(1+home equityij t )”, we include in the sample those with zero home equity
too (i.e., renters and homeowners whose mortgage equals 100%). However,
we also run our regressions after excluding renters, in order to exploit further
the richness of our sample, which allows us to compare the extensive margin
of entrepreneurship within the sample of homeowners. Consistent with the
hypothesis, we expect that β2 > 0.

Xij t denotes a vector of demographic and income proxies. These include
age, education, race, gender, marital status, and employment status, for each
household i in state j at time t . Variables related to liquidity constraints are
also included in Xij t , in particular, the household’s current labor income and
net wealth. Not accounting for wealth may bias our results upward because
wealthier individuals may be simultaneously more likely to own a more
expensive house and to become entrepreneurs. Theory predicts that the inability
to acquire the capital necessary to start a business is one of the main theoretical
obstacles faced by would-be entrepreneurs. A large literature has documented
a positive relationship between initial wealth and subsequent business entry
(e.g., Evans and Jovanovic 1989; Evans and Leighton 1989; Fairlie 1999;
Quadrini 1999). However, Hurst and Lusardi (2004) challenge this view. Using
detailed survey data, they show that personal wealth is important only for the
richest households, and that once these are excluded from the sample, there

5 Corradin and Popov (2013) formulate a simple model of career choice with homeownership in the presence
of borrowing constraints to motivate this hypothesis. In their model, agents start out as fixed-wage workers
and can choose to become entrepreneurs in each following period. Their value function in each period depends
on own wealth (as in Quadrini 2000; Cagetti and De Nardi 2006; and Buera 2009), but also on house prices
through two channels. The first is that the current level of house prices affects the equity value they can extract
from their home, increasing their mortgage balance. The housing stock thus has a collateral value component
beyond the value derived from the direct consumption of housing services, due to prospects of limits on the
amount of borrowing. The second channel results from the interaction between the first channel and the cost
of external financing available to the potential entrepreneur. As in Quadrini (2000), the entrepreneur can take
external financing through a business loan up to an amount that corresponds to the difference between the
maximum permitted level of capital investment and total net wealth. The model thus predicts that agents are
more likely to become entrepreneurs when the value of their home equity is high. It also predicts that on average,
new entrepreneurs increase their mortgage balance and rely more on external financing.

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is no statistical relationship between wealth and entrepreneurial choices. If
the positive relationship between wealth and entrepreneurship is confined to
the top of the wealth distribution, then self-employment contains a “luxury
good” component; namely, as households become wealthier, they are more
likely to purchase the benefits associated with owning a business, such as
prestige, power over decision-making, or flexible time schedule.6 We take
these considerations into account by showing that our main results are robust
to dropping the wealthiest 5% or 10% of the households from the sample.7

Finally, �j t is a matrix of MSA×year fixed effects. These control for any
remaining time-varying factors that are common to all households in an MSA.
Consequently, the estimate of β2 is driven off comparisons of households in
the same MSA and year who have access to different levels of housing wealth.

While this empirical model allows us to evaluate the association between
current home equity and future small business ownership, there are a number
ways in which the probit estimates could be biased. The most obvious one is
related to omitted variable bias. For example, credit standards were gradually
relaxed throughout the United States in the wake of the dot-com bust. Easy
credit may have simultaneously generated an increase in house values by raising
local demand for housing and a decrease in borrowing limits for new firms. In
other words, credit standards may have declined relatively more in areas with
rising house prices, a possibility corroborated by the evidence of Mian and Sufi
(2011) and Duca, Mullbauer, and Murphy (2011). Alternatively, individuals
who ultimately become entrepreneurs may disproportionately reside in areas
with local economies in which the propensity to start a business is naturally
higher. If the areas that experienced the largest house price booms during the
early to mid-2000s happen to be latently entrepreneurial too (such as California,
Massachusetts, and New York), then a positive association between home equity
and new business creation may again be largely driven by an omitted variable
bias. And even if changes in home equity are uncorrelated with credit standards
or with unobservable entrepreneurial tendencies, rising house prices can feed
back into local demand booms, raising the return to entrepreneurship and,
subsequently, the rate of new business creation. In this case, home equity will
be related to entrepreneurship though a different channel than the home equity
borrowing channel we seek to identify.

To isolate a clear causal effect of housing wealth on entrepreneurship, we
need to identify exogenous shocks to home equity which are unrelated to local
entrepreneurial propensity or economic conditions. To achieve this, we use
an instrumental variable procedure to generate exogenous variation in home

6 In the same vein, Hamilton (2000) shows that most entrepreneurs enter and persist in business, although they have
both lower initial earnings and lower income growth than paid employees. Moskowitz and Vissing-Jorgensen
(2002) show that the returns on private equity are no higher than the returns on public equity, even though
entrepreneurial investment is poorly diversified. Both papers thus suggest that there are important nonpecuniary
benefits of entrepreneurship.

7 The results from these tests are reported in the Internet Appendix.

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equity. Building on the research design of Chetty and Szeidl (2010), we first
calculate, for each homeowner in the sample, the change in national (U.S.-
wide) house prices between the year when the house was bought and the
current year. We also use information on the local topological elasticity of
housing supply from Saiz (2010). The rationale is that local adjustment in
response to changes in the demand for housing is more likely to take place on
the quantity margin in places with highly elastic housing supply, and on the
price margin in areas with inelastic housing supply.8 We calculate this elasticity
both at the state and at the MSA level.9 The second approach is preferred as it
yields a higher variation in the local elasticity of housing supply; however, for
anonymity purposes, the MSA is not reported for 37% of households, and so
in these cases, we apply the state-level elasticity.10 We then divide the change
in national house prices between the year when the house was bought and the
current year by the local elasticity of housing supply. The resulting ratio is our
instrument for home equity. The idea is that following a positive aggregate
U.S.-wide demand shock for housing, house prices will appreciate (and
consequently home equity will increase, keeping the mortgage balance
constant) considerably more in local areas with less elastic housing supply due
to topological reasons, where adjustment in response to such shocks takes place
on the price margin. It is therefore reasonable to expect that this instrument will
help identify the variation in home equity that is unrelated to changes in local
business conditions.

In addition to constructing an instrument for home equity, we implement a
number of other strategies aimed at identifying the effect. First, we repeat our
analysis only on the subsample of individuals who live in the MSAs with the
most elastic housing supply, such as the ones in the Great Plains. In those, the
propensity to start a business is less likely to be correlated with the local price
response to economy-wide changes in housing demand. Second, by excluding
from the analysis business start-ups in industries such as construction, finance,
and real estate, we seek to alleviate concerns that the house price boom is driving
entrepreneurship through a demand channel rather than through a home equity
borrowing channel. Finally, we compare the change in mortgage debt of new
business owners and of nonbusiness owners around the same point in time in
the same geographic locality. If new business owners can indeed efficiently tap
into housing wealth, one should observe a higher increase in mortgage debt,
from one period to another, for new business owners relative to the rest.

8 Other recent papers to pursue identification of changes in collateral values using the local topological elasticity
of housing supply include Chaney, Sraer, and Thesmar (2012), Adelino, Schoar, and Severino (Forthcoming),
and Cvijanovic (2014), among others.

9 The original data in Saiz (2010) are at the MSA level. We calculate state-level elasticities by averaging the
MSA-level elasticities for all MSAs in the state, weighted by the MSA’s population.

10 The SIPP stopped reporting the MSA field starting with the 2004–2006 wave.

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3. Empirical Results

We now present different sets of empirical estimates of model (1). In
Section 3.1, we present the evidence from probit regressions on the association
between housing wealth and new business creation, alongside a batter of
robustness checks. In Section 3.2, we present the headline results in the paper,
where we account for various sources of endogeneity in order to be able to
interpret the positive effect of housing wealth on new business creation as a
causal one.

3.1 Preliminary results: Probit estimation
3.1.1 Main result. In Table 4, we report the results from the base probit
estimation of model (1). We start with all households who do not own or operate
a business in the first period when they were interviewed (Columns (1) and (2)),
and then we repeat the tests on the subsample of homeowners who do not own
or operate a business in the first period when they were interviewed (Columns
(3) and (4)). We control for the demographic characteristics of the household
(age, education, race, gender, marital status, labor income, and employment
status). To clearly separate the effect of housing wealth from the effect of the
rest of the household’s financials, we report specifications without (Columns
(1) and (3)) and with (Columns (2) and (4)) nonhousing wealth and the size of
the household’s mortgage.

The estimates suggest that higher housing wealth significantly increases the
probability that a household makes the transition to business ownership in the
next period. Neither the coefficients nor their significance appears to be sensitive
to the inclusion of nonhousing wealth and of mortgage debt. The intuition for
the observed effect is that when house prices increase and raise the value of
the property, holding the mortgage fixed, individuals can now extract more
housing wealth from the house to buy working capital if they are to switch
from employment to self-employment. In all cases, the null hypothesis that
current home equity has no effect on future business ownership is rejected with
p < 0.05. The effect is of a sizeable magnitude. Take the specification with individual and financial controls and MSA×year fixed effects in the case of homeowners (Column (4)). Going from the 25th to the 75th percentile of past house price increases (corresponding to a 17.70% increase) implies that the average house in the sample (valued at $87,659) appreciated by $15,516, or a 35% increase in home equity, holding the mortgage constant. The estimate of the marginal effect implies that for a household with the sample mean demographic and income characteristics, an 35% increase in home equity raises the probability that the household head will transition into entrepreneurship in the future by 0.35×0.0011=0.039 percentage points. Given a mean share of households who transition into entrepreneurship in the next period of 1.0%, this is equivalent to a 3.9% increase in the probability that the household will transition into entrepreneurship in the next period.

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Table 4
Business ownership and home equity: Probit results

Business owner next period

Owners and renters Owners

(1) (2) (3) (4)

Log (1 + Home equity) 0.0211∗∗∗ 0.0156∗∗∗ 0.0288∗∗∗ 0.0243∗∗
(0.0039) (0.0053) (0.0089) (0.0111)

Age 31–40 0.0492 0.0273 0.1014 0.0982
(0.0725) (0.0825) (0.0748) (0.0833)

Age 41–50 0.0122 −0.0451 0.0678 0.0312
(0.0757) (0.0842) (0.0834) (0.0916)

Age 51–60 −0.0710 −0.1413 −0.0258 −0.0724
(0.0738) (0.0917) (0.0900) (0.1013)

High school 0.0297 0.0814 0.0409 0.0485
(0.0545) (0.0663) (0.0788) (0.0877)

Some college 0.0526 0.1003 −0.0084 −0.0127
(0.0721) (0.0832) (0.0943) (0.1013)

College or more 0.1494∗ 0.1545∗ 0.1128 0.0561
(0.0779) (0.0927) (0.1027) (0.1116)

African American −0.1754∗∗ −0.1130 −0.1917∗ −0.1196
(0.0793) (0.0806) (0.1135) (0.1074)

Female −0.0782∗ −0.0892∗∗ −0.0461 −0.0560
(0.0421) (0.0417) (0.0553) (0.0538)

Married 0.0974∗∗∗ 0.0792∗∗ 0.1066∗∗ 0.0957∗
(0.0336) (0.0376) (0.0441) (0.0507)

Log (1 + Labor income) 0.4151 −0.2117 −0.1724 −0.9184
(0.3856) (0.4789) (0.5773) (0.5909)

Unemployed 0.1080 0.0697 −0.0348 −0.0167
(0.1151) (0.1314) (0.1166) (0.1160)

Log (1 + Household nonhousing wealth) 0.1894∗∗∗ 0.2048∗∗∗
(0.0424) (0.0414)

Log (1 + Home mortgage) 0.0047 0.0088
(0.0053) (0.0058)

MSA × year fixed effects Yes Yes Yes Yes
Marginal effect of increasing home equity 0.0006 0.0006 0.0010 0.0011
Observations 38,421 36,473 23,123 22,301
Pseudo R-squared 0.05 0.06 0.04 0.05

The table reports probit estimates of transition into business ownership in the next period. The sample is composed
of all households in SIPP for the 1996–2000, 2001–2003, and 2004–2006 waves between the age of 22 and 60
that did not operate a business the first time they were interviewed (Columns (1) and (2)) and of all households
that owned a home and did not own and operate a business the first time they were interviewed (Columns (3) and
(4)). “Home equity” denotes the difference between the value of the household’s property and the value of the
household’s mortgage. “Age 31–40” is a dummy variable equal to one if the household head’s age is between 31
and 40 years. “Age 41–50” is a dummy variable equal to one if the household head’s age is between 41 and 50
years. “Age 51–60” is a dummy variable equal to one if the household head’s age is between 51 and 60 years.
“High school” is a dummy variable equal to one if the household head has finished at most high school. “Some
college” is a dummy variable equal to one if the household head is a college dropout. “College or more” is a
dummy variable equal to one if the household head has at least a college degree. “African American” is a dummy
variable equal to one if the household head is African American. “Female” is a dummy variable equal to one if
the household head is a female. “Married” is a dummy variable equal to one if the household head is married.
“Labor income” denotes the annual household income from supplied labor. “Unemployed” is a dummy variable
equal to one if the household head is currently unemployed. “Household nonhousing wealth” denotes the total
wealth of the household net of the amount of home equity. “Home mortgage” denotes the value of the household’s
mortgage. Omitted category in age is “Age 22–30.” Omitted category in education is “High school dropout.”
Standard errors clustered by state are reported in parentheses, where ***, **, and * indicate significance at the
1%, 5%, and 10% level, respectively.

We also find that individuals are more likely to become entrepreneurs if
they have at least a college degree and if they are white, male, and married,
which is consistent with the results of Hurst and Lusardi (2004). The effect

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Table 5
Business ownership and home equity: Robustness

Business Business Business
owner owner or owner

Business next period, Business business next Business
owner OLS equity equity period, owner

next period, + sample next next all in the
OLS weights period period years future

(1) (2) (3) (4) (5) (6)

Log (1 + Home equity) 0.0003** 0.0004** 0.0121*** 0.0167*** 0.0129*** 0.0186***
(0.0001) (0.0002) (0.0037) (0.0040) (0.0049) (0.0046)

Individual controls Yes Yes Yes Yes Yes Yes
MSA × year fixed effects Yes Yes Yes Yes Yes Yes
Marginal effect of 0.0009 0.0014 0.0003 0.0006

increasing home equity
Observations 42,464 42,464 50,881 51,469 46,840 37,895
(Pseudo) R-squared 0.01 0.01 0.04 0.04 0.05 0.06

The table reports OLS estimates (Columns (1) and (2)) and probit estimates (Columns (3)–(6)) of transition into
business ownership. The independent variable is a dummy equal to one if the household head owns and operates
a business next period (Columns (1) and (2) and (5)) or in any future period (Column (6)), a dummy equal to
one if the households owns business equity next period (Column (3)), or a dummy equal to one if the household
owns and operates a business or owns business equity next period (Column (4)). The sample is composed of all
households in SIPP for the 1996–2000, 2001–2003, and 2004–2006 waves between the age of 22 and 60 that
owned a home and did not own and operate a business (Columns (1) and (2) and (5) and (6)), did not own business
equity (Column (3)), or did not own business equity or own and operate a business (Column (4)) the first time they
were interviewed. In Column (5), all observations during each wave for each nonbusiness owning household are
included. “Home equity” denotes the difference between the value of the house hold’s property and the value of
the household’s mortgage. All individual controls from Table 4 are included. In Column (2), the survey-provided
household weights are used to account for the fact that SIPP oversamples low-income households. Standard
errors clustered by state are reported in parentheses, where ***, **, and * indicate significance at the 1%, 5%,
and 10% level, respectively.

of nonhousing wealth is positive and significant, implying that other types of
wealth that can be used to finance a business also increase the probability of
transition into entrepreneurship. The point estimate implies that variations in
home equity have a twice bigger effect on entrepreneurship than variations
in nonhousing wealth, which may partially be due to the fact that nonhousing
wealth includes the value of durables, such as vehicles and nonhome real estate.
Finally, the effect of mortgage debt is positive but insignificant, implying that
controlling for housing and nonhousing wealth, households with additional
leverage are no more likely to start a business.

3.1.2 Robustness. Next, we perform, in Table 5, a battery of alternative tests
to evaluate the robustness of the statistical association between housing wealth
and new business creation, for the full sample of owners and renters. We start
by recognizing that in parametric panel data models with fixed effects, where
the independent variable is a dummy variable, the estimates will in general
be inconsistent if the time dimension is small (Neyman and Scott 1948). As
a solution to this “incidental variables” problem, we re-evaluate model (1)
using a linear probability model instead of a nonlinear probability one. As
Column (1) of Table 5 reports, the significant positive association between
current home equity and future transition into entrepreneurship remains robust
to this alternative specification.

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In Column (2) of Table 5, we address another possible concern related
to our methodology, namely, the fact that our data may not be derived
from a representative survey. The SIPP intentionally oversamples low-income
households to maximize the coverage of households getting public services.
Indeed, median labor income in the sample is $38,400 compared with mean
labor income of $48,998, and median nonhousing wealth is five times smaller
than the mean one, implying that the income and wealth distribution in our
dataset is positively skewed. We address this issue by performing a version
of the empirical test in Column (1) where we have used the reported survey
weights to reweight our observations. As reported in Column (2), this alternative
methodology does not yield results that are qualitatively or quantitatively very
different from our baseline estimates.11

Another concern is that the positive association between housing wealth and
entrepreneurship can be driven by our choice of proxy for entrepreneurship. We
have defined an entrepreneur as an individual who owns and operates a business.
While accurate, this definition of entrepreneurship by construction does not
account for indirect business ownership. We now replicate our preferred
specification after employing alternative definitions of business ownership
that have been used in the literature. We first define transition into business
ownership as a dummy variable equal to one if the household declares zero
business equity in the current period but positive business equity in the next
period. This definition accounts for the fact that individuals may become
business owners not only by starting a business themselves but also by investing
in other agents’ (such as family members’) business. Column (3) of Table 5
reports the estimates from this test. The null hypothesis that current home equity
has no effect on future business equity is rejected at the 1% statistical level.
In Column (4) we employ the definition of entrepreneurship used by Hurst
and Lusardi (2004); namely, we define business owners as households who are
either running and operating their own business or own business equity. We
find that the positive association between home equity and the probability of
transition into business ownership continues to be statistically strong (p < 0.01) for this broadest possible definition of entrepreneurship. We conclude that our results so far are not driven by our choice of proxy for new business creation.

A fourth concern is related to the timing of transition into entrepreneurship.
In our main empirical tests we have focused on the moment when non-business-
owning households were interviewed for the first time, and we have then looked
at the probability that they would start a business in the next period. Given
that in two of the three SIPP waves there are more than two observations
per household, this leaves out a potentially substantial number of start-ups.
We now address this issue in two ways. First, we look at the probability of

11 The estimates in Columns (1) and (2) are directly comparable to the marginal effects reported in Table 4.

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transitioning into entrepreneurship in the next period at all times, not just the
first time when households were interviewed. This allows us to include multiple
observations of the same household. The estimates reported in Column (5)
of Table 5 suggest that our results so far have not been affected materially
by the exclusion of a number of later start-ups. In Column (6), we address
the same issue differently by defining entrepreneurship as the probability of
a nonbusiness owner becoming an entrepreneur at any point in the future.
There are 678 such households, compared with 453 who transition in the next
period. This approach suffers from a purely mechanical problem in that different
waves of the SIPP include a different number of years, and so households’s
entrepreneurial choices are observed over different durations in each panel.
At the same time, it allows us to capture a larger number of start-ups without
including the same household in the regression multiple times. The results
remain qualitatively unchanged: the strong positive association between home
equity and transition to entrepreneurship survives this alternative definition of
the switching horizon. Again, the null hypothesis that current home equity has
no effect on future business ownership is rejected with p < 0.01. Quantitatively, the probit estimate increases to 0.0236 (from 0.0210 in Table 4, Column (2)). This is expected given that the probability of transitioning into entrepreneurship is higher over a longer horizon.

Importantly, all results in Table 5 are recorded after including our exhaustive
set of variables, which proxy for demographic characteristics, nonhousing
wealth, and mortgage debt. We conclude that the positive association between
home equity and new business creation is not due to the high correlation between
home equity and net wealth, disposable income, changes in the return to
entrepreneurship, or to the potential impact of homeownership on the propensity
for business ownership through the channel of higher leverage.

Our main results seem at odds with those of Hurst and Lusardi (2004), who
use microlevel data from the PSID to argue that liquidity constraints do not
matter for entrepreneurship for most of the wealth distribution. Furthermore,
they find that households that live in regions in which house prices appreciated
strongly are no more likely to start a business than are households in other
regions. We believe that a number of factors can explain the difference between
their work and ours. First, their sample consists of around 8,000 individuals
(compared with around 79,000 in our sample), so they are not able to exploit
as rich a regional variation in house price changes as we are. Second, their
analysis is conducted on data from the 1989–1994 period when house prices
in the United States were relatively flat. Conversely, we are able to exploit the
effect of the large increase in house prices during the early to mid-2000s, when
in some states (such as California) house prices doubled over the span of five
years.

3.1.3 Interaction effects. Our results suggest that credit constraints serve as
a barrier to new business creation and that a credit expansion based on an

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increase in home equity borrowing can help nascent entrepreneurs overcome
this barrier. This raises a number of additional questions related to the link
between house prices, the business cycle, and credit expansion. First, is the
home equity borrowing channel of housing more potent for individuals that are
more credit constrained? If households have sufficient nonhousing wealth, such
as savings they can tap into or durable nonhousing goods they can liquidate,
then the effect of additional home equity should not have a material effect on
the propensity to start a business, and so the association we uncover might be
spurious. Second, does the effect of an increase in housing wealth vary along
the business cycle? In particular, the early to mid-2000s saw an unprecedented
expansion in credit supply in general and in home equity borrowing, in
particular (Mian and Sufi 2009), whereas home equity borrowing may not
have been so prevalent in earlier decades. In fact, Hurst and Lusardi (2004)
analyze the early 1990s, a period notable for a sharp credit crunch, which
many hold accountable for the 1991 recession (e.g., Bernanke and Lown 1991).
Finally, which role do characteristics of the local business environment, such
as the local business cycle or legal treatment of housing assets, play in the
association between housing wealth and new business creation? Because our
specifications include MSA × year fixed effects, our tests cannot say anything
on the level effect of those, but we could still study how they interact with
housing wealth.

In Table 6, we address these questions by including interactions of home
equity with demographic, financial, and state-specific variables, as well as
year dummies, in our main model. We observe three sets of facts. First, while
the coefficient on the interaction of home equity with nonhousing wealth is
insignificant, the coefficient on the interaction of home equity with labor income
is negative and significant, suggesting that housing wealth is more important
for individuals with a lower labor income. The evidence thus implies that
housing wealth stimulates new business creation relatively more for credit
constrained households. Second, we find that the same level of housing wealth
has a similar effect on new business creation in the 1990s and 2000s, as the
coefficient on the interaction of home equity with the 2002 dummy and the 2005
dummy is insignificant.12 Finally, we also find that the interactions of state-level
unemployment and of the level of the homestead exemption with home equity
is positive and significant. The first piece of evidence confirms the intuition that
for the same level of housing wealth, individuals are more likely to transition
into self-employment when job opportunities are scarce. The second suggests
that for homeowners with higher housing wealth, the direct effect of higher
homestead exemptions in the case of personal bankruptcy through reduced
business risk dominates the indirect effect through endogenously tighter credit
constraints.

12 The omitted year category is the 1997 dummy.

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Table 6
Business ownership and home equity: Interaction with individual and state characteristics

Business owner next period

Age 31–40 × Log (1 + Home equity) 0.0176
(0.0125)

Age 41–50 × Log (1 + Home equity) 0.0194∗
(0.0105)

Age 51–60 × Log (1 + Home equity) 0.0304∗
(0.0172)

High school × Log (1 + Home equity) 0.0190
(0.0161)

Some college × Log (1 + Home equity) −0.0179
(0.0122)

College or more × Log (1 + Home equity) −0.0099
(0.0175)

African American × Log (1 + Home equity) −0.0017
(0.0141)

Female × Log (1 + Home equity) 0.0111
(0.0093)

Married × Log (1 + Home equity) 0.0007
(0.0071)

Log (1 + Labor income) × Log (1 + Home equity) −0.3210∗∗∗
(0.0990)

Unemployed × Log (1 + Home equity) −0.0204
(0.0232)

Log (1 + Household nonhousing wealth × Log (1 + Home equity) 0.0097
(0.0097)

Log (1 + Home mortgage) × Log (1 + Home equity) 0.0015
(0.0023)

State unemployment × Log (1 + Home equity) 0.0071∗
(0.0040)

GSP growth × Log (1 + Home equity) −0.0022
(0.0022)

Homestead exemption × Log (1 + Home equity) 0.0018∗
(0.0011)

Dummy 2002 × Log (1 + Home equity) −0.0180
(0.0144)

Dummy 2005 × Log (1 + Home equity) −0.0049
(0.0105)

Individual controls Yes
MSA × year fixed effects Yes
Observations 36,378
Pseudo R-squared 0.06

The table reports probit estimates of becoming a business owner in the next period. The sample is composed of
all households in SIPP for the 1996–2000, 2001–2003, and 2004–2006 waves between the age of 22 and 60 that
did not own and operate a business the first time they were interviewed. “Home equity” denotes the difference
between the value of the household’s property and the value of the household’s mortgage. “Age 31-40” is a
dummy variable equal to one if the household head’s age is between 31 and 40 years. “Age 41-50” is a dummy
variable equal to one if the household head’s age is between 41 and 50 years. “Age 51-60” is a dummy variable
equal to one if the household head’s age is between 51 and 60 years. “High school” is a dummy variable equal
to one if the household head has finished at most high school. “Some college” is a dummy variable equal to one
if the household head is a college dropout. “College or more” is a dummy variable equal to one if the household
head has at least a college degree. “African American” is a dummy variable equal to one if the household head
is African American. “Female” is a dummy variable equal to one if the household head is a female. “Married” is
a dummy variable equal to one if the household head is married. “Labor income” denotes the annual household
income from supplied labor. “Unemployed” is a dummy variable equal to one if the household head is currently
unemployed. “Household nonhousing wealth” denotes the total wealth of the household net of the amount of
home equity. “Home mortgage” denotes the value of the household’s mortgage. “State unemployment” denotes
the current rate of unemployment in the household’s state. “GSP growth” denotes the current rate of gross
state product growth in the household’s state. “Homestead exemption” denotes the current rate of exemption in
bankruptcy of one’s property in the household’s state. “Dummy 2002” is a dummy variable equal to one if the
household was first interviewed in 2002. “Dummy 2005” is a dummy variable equal to one if the household
was first interviewed in 2005. Omitted category in age is “Age 22–30.” Omitted category in education is “High
school dropout.” Omitted category in year is 1997. All individual controls from Table 4 are included. Standard
errors clustered by state are reported in parentheses, where ***, **, and * indicate significance at the 1%, 5%,
and 10% level, respectively.

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3.2 Headline results: Instrumental variables estimation
3.2.1 Ruling out omitted variable bias. While our probit estimates establish
a robust positive association between current home equity and future new
business creation, there are a number of ways in which the probit estimates
could be biased. The most obvious is related to omitted variable bias. For
example, credit standards were gradually relaxed throughout the United States
in the wake of the dot-com bust. Easy credit may have simultaneously generated
an increase in house values by raising local demand for housing and a decrease
in borrowing limits for new firms. Mian and Sufi (2011) and Duca, Mullbauer,
and Murphy (2011) document a larger decline in credit standards in local areas
with a house price boom. Along the same lines, Corradin (2014) suggests that
if mortgage lenders had optimistic views on house price dynamics in terms of
low house price volatility and/or high expected house price growth rate, they
may have loosened underwriting standards and offered low down payments
contracts. Alternatively, individuals who ultimately become entrepreneurs may
disproportionately reside in areas in which the propensity to start a business
is naturally higher. If the areas that experienced the largest house price booms
during the early to mid-2000s happen to be latently entrepreneurial, too (such as
California, Massachusetts, and New York), then a positive association between
home equity and new business creation again may be largely driven by an
omitted variable bias. And even if changes in home equity are uncorrelated
with credit standards or with unobservable entrepreneurial tendencies, rising
house prices can feed back into local demand booms, raising the return to
entrepreneurship and, subsequently, the rate of new business creation. In this
case, home equity will be related to entrepreneurship through a different channel
than the collateral channel we seek to identify.

To isolate a clear causal effect of home equity on entrepreneurship through
the collateral channel, we need to identify exogenous shocks to home equity
that are unrelated to local entrepreneurial propensity or economic conditions. To
achieve this, we use an instrumental variable procedure to generate exogenous
variation in home equity. Building on the research design in Chetty and Szeidl
(2010), we first calculate, for each homeowner in the sample, the change in
national (U.S.-wide) house prices between the year when the house was bought
and the current year. Next, we use information on the local topological elasticity
of housing supply from Saiz (2010). The idea is that local adjustment in response
to changes in the demand for housing is more likely to take place on the quantity
margin in places with a highly elastic housing supply (i.e., flat areas, such as
Kansas) and on the price margin in areas with inelastic housing supply (i.e.,
coastal cities, such as Boston or San Francisco). We calculate this elasticity
both at the state level and at the MSA level. The second approach is preferred
as it yields a higher variation in the local elasticity of housing supply (there
are ninety-six reported MSAs). The downside is that for about one-third of
the households in SIPP, the MSA is not reported for confidentiality reasons,
and so we assign the state-level value to these households. We then divide, by

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Table 7
Business ownership and home equity: IV results

Business owner next period: Owners

First stage Second stage Reduced form

(1) (2) (3) (4) (5) (6)

House price growth/MSA elasticity 17.882*** 2.278*** 0.2462 0.1663
(0.223) (0.197) (0.2384) (0.2481)

Log (1 + Home equity) 0.1304* 0.1617* 0.0377* 0.0285
(0.0886) (0.1064) (0.2233) (0.0222)

Wald F -statistics 6424.81 134.29
Individual controls Yes Yes Yes Yes Yes Yes
Nonhousing wealth and Home No Yes No Yes No Yes

mortgage included
MSA × year fixed effects Yes Yes Yes Yes Yes Yes
Marginal effect of increasing 0.0053 0.0068

home equity
Observations 38,771 22,946 19,353 19,346 19,202 19,137
Pseudo R-squared 0.72 0.22 0.03 0.03 0.04 0.05

The table reports estimates from OLS (Columns (1) and (2)) and from probit (Columns (3)–(6)) regressions. In
Columns (1) and (2), the dependent variable is “Log (1 + home equity).” In Columns (3)–(6), the dependent
variable is a dummy variable equal to one if the household owns or operates a business in the next period. The
sample is composed of all households in SIPP for the 1996–2000, 2001–2003, and 2004–2006 waves between
the age of 22 and 60 that owned a home and did not own and operate a business the first time they were
interviewed. “Home equity” denotes the difference between the value of the household’s property and the value
of the household’s mortgage. “House price growth” is the U.S.-wide change in house prices between the year
when the property was bought and the current year. For renters, it is equal to zero. “MSA elasticity” is the MSA’s
elasticity of housing supply (from Saiz 2010). In Columns (3) and (4), “Log (1 + home equity)” is instrumented
using the U.S.-wide change in house prices between the year when the house was bought and the present year,
divided by the MSA’s elasticity of housing supply. All individual controls from Table 4 are included. Standard
errors clustered by state are reported in parentheses, where ***, **, and * indicate significance at the 1%, 5%,
and 10% level, respectively.

the topological elasticity of housing supply, the change in national house prices
between the year when the house was bought and the current year. The resulting
ratio is our instrument for home equity. The main idea is that when there is an
aggregate demand shock for housing at the national level, house prices will
appreciate (and consequently home equity will increase) considerably more
in MSAs with less elastic housing supply due to topological reasons, where
adjustment in response to such shocks takes place on the price margin. It is
therefore reasonable to expect that this instrument will help identify a variation
in home equity that is unrelated to local business conditions. Furthermore, this
instrument takes on different values for different individuals in the same MSA
and year, and as a result, the housing wealth coefficient is driven solely off
the comparison of people in the same MSA and year who have experienced
different housing price appreciation based solely on differences in the year in
which they purchased their home.

In Table 7, we report the estimates from model (1), where we use the
instrumental variable just described to extract the exogenous component of
home equity. Because for renters, we would have to arbitrarily set the value
of the instrument to zero, we run our tests on the sample of homeowners
only. We first report the estimates from the first-stage regression of the
natural logarithm of home equity on the instrumental variable. We do so after

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excluding (Column (1)) and including (Column (2)) the natural logarithms of
two potentially endogenous financial variables: nonhousing wealth and housing
leverage. The reported coefficients imply that the change in national house
prices between the year of purchase and the current year, divided by the local
elasticity of housing supply, predicts a large share of the variation in individual
housing wealth. The value of the first-stage Wald statistics, reported as “Wald F-
statistics,” is strictly higher than the critical value for the IV regression to have
no more than 10% of the bias of the probit estimate (see Stock and Yogo 2005).

Next, we report the estimates from the second-stage regression, again with
and without proxies for nonhousing wealth and the size of the mortgage. Both
variables are endogenous: individuals planning to start businesses in the future
may save more today, thus paying down mortgage debt and amassing more
financial assets. Because we do not have enough instruments for all endogenous
variables, we report two versions of each regression, one that does not control
for nonhousing wealth and for the size of the mortgage, and one that does.

The estimates in Columns (3) and (4) strongly suggest that the null hypothesis
that current housing wealth has no effect on future business ownership is
rejected. In both cases, the effect is significant at the 10% statistical level.
Once again, the effect is of a sizeable magnitude. Take the specification with
individual and financial controls and MSA × year fixed effects (Column (4)).
Going from the 25th to the 75th percentile of past house price increases
(corresponding to a 17.70% increase in property values) implies that the average
house in the sample (valued at $87,659) appreciated by $15,516. Given an
average home equity in the sample of $44,111, this corresponds to a 35%
increase in home equity, holding the mortgage constant. The point estimate
implies that for a household with the sample mean demographic and income
characteristics, a 35% increase in home equity raises the probability that the
household head will transition into entrepreneurship in the next period by
0.35×0.0068 = 0.24 percentage points. Given a mean share of households who
transition into entrepreneurship in the next period of 1.0%, this is equivalent
to a 24% increase in the probability that the household will transition into
entrepreneurship in the next period.13

For an alternative interpretation, consider the following back-of-the-
envelope calculation. The average difference in business equity between a
new business owner and a nonbusiness owner is $24,500 (see Table 3), which
corresponds to 55% of average home equity in the sample. Our estimate in
Column (4) of Table 7 implies that the 55% increase in home equity necessary
to generate the average difference in business equity between business owners
and nonbusiness owners in the sample increases the probability of switching to
self-employment by 38.5%. There were roughly 110 million households in the

13 This effect is directly comparable to Schmalz, Sraer, and Thesmar (2013), who find that going from the 25th to
the 75th percentile of past house price growth increases the probability of firm creation by homeowners, relative
to renters, by up to 13%.

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The Review of Financial Studies / v 28 n 8 2015

United States in 2006, around 1% of whom (or 1.1 million households) switch
to self-employment each year. Therefore, the 55% increase in home equity
required to generate the average difference in business equity between business
owners and nonbusiness owners results in an additional 423,500 households
who switch from fixed income to self-employment, year-on-year.

The validity of the identification strategy rests on the assumption that an
exogenously generated change in house prices is a legitimate instrument for
housing wealth in model (1). The first-stage estimates reported in Columns (1)
and (2) of Table 7 suggest that the instrument is strongly related to housing
wealth. However, for the IV estimate to be consistent, it must also be the
case that the instrument is uncorrelated with the residual in model (1). If the
instrument influences new business creation for reasons other than changes in
individual housing wealth, our approach is called into question. As a way of
addressing this concern, we note that if the only impact of the instrument is
through changes in housing wealth, then the instrument should be insignificant
if included in model (1). Columns (5) and (6) report the estimates from this
reduced form model. The test fails to reject the null hypothesis that the effect
of U.S.-wide changes in housing prices, normalized by the MSA’s elasticity of
housing supply, equals zero. Furthermore, when we compare the top-half to the
bottom-half of the MSAs in terms of elasticity, we find that this probability of
transitioning into entrepreneurship in the next period is 1.01% in the former and
0.94% in the latter and that the difference between the two is not significant.
We find the same when we compare the top tertile to the bottom tertile.

3.2.2 Ruling out local demand booms and feedback effects. Our
identification strategy addresses head-on the concern that the positive
association between home equity and entrepreneurship may be driven by local
economic conditions that simultaneously raise house prices and increase new
business formation. Nevertheless, one concern with identification still remains,
namely, that (even exogenous) changes in home prices can feed back into local
demand booms. Mian, Rao, and Sufi (2013) document that households consume
a large share of any additional home equity. If the demand for entrepreneurial
services (or a subset of these) goes up relatively more in areas with the highest
increase in home prices, then our estimates of the size of the home equity
borrowing channel can still be biased upwards.

We have so far partially dealt with this possibility by comparing owners
and renters, whereby the latter entirely lack access to home equity borrowing,
as well as by employing lagged explanatory variables whereby we map home
equity at time t into the probability of transition into entrepreneurship at time
t + 1. In Table 8, we address this concern in two additional ways. First, we
repeat our tests on a subsample of MSAs with the highest elasticity of housing
supply. In these, adjustment in the housing supply is relatively quick, and so
it is unlikely that an aggregate demand shock would raise prices enough to
generate an empirically substantial local demand boom. Second, we exclude

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Table 8
Business ownership and home equity: Ruling out demand booms and feedback effects

High-elasticity MSAs
High-elasticity Excluding housing and excluding housing

MSAs boom-driven sectors boom-driven sectors

Probit IV Probit IV Probit IV

(1) (2) (3) (4) (5) (6)

Log (1 + Home equity) 0.0269*** 0.0447*** 0.0159*** 0.0223** 0.0280*** 0.0539***
(0.0059) (0.0146) (0.0051) (0.0121) (0.0053) (0.0168)

Individual controls Yes Yes Yes Yes Yes Yes
MSA × year fixed effects Yes Yes Yes Yes Yes Yes
Marginal effect of increasing 0.0008 0.0013 0.0004 0.0006 0.0008 0.0016
home equity
Observations 19,292 18,245 36,308 34,848 19,276 18,221
Pseudo R-squared 0.06 0.05 0.05 0.01 0.06 0.05

The table reports probit estimates of becoming a business owner in the next period. The sample is composed of
all households in SIPP for the 1996–2000, 2001–2003, and 2004–2006 waves between the age of 22 and 60 that
did not own and operate a business the first time they were interviewed. “Home equity” denotes the difference
between the value of the household’s property and the value of the household’s mortgage. In Columns (2), (4),
and (6), “Log (1 + home equity)” is instrumented using the U.S.-wide change in house prices between the year
when the house was bought and the present year, divided by the MSA’s elasticity of housing supply (from Saiz
2010). In Columns (1) and (2) and (5) and (6), only individuals living in the top 50% of the MSAs in terms of
elasticity of housing supply are included. In Columns (3)–(6), the following sectors are excluded: Construction;
Finance, insurance, real estate, and rental and leasing; and Education, health, and social services. All individual
controls from Table 4 are included. Standard errors clustered by state are reported in parentheses, where ***, **,
and * indicate significance at the 1%, 5%, and 10% level, respectively.

from the sample those entrepreneurs entering businesses that are directly linked
to the housing boom. Two obvious such businesses are construction firms and
mortgage brokerages. If all new small businesses in the sample are related to
the building or selling of real estate property, then it will be hard to argue for a
home equity borrowing channel of housing. This strategy is somewhat related to
that of Adelino, Schoar, and Severino (Forthcoming), who separate the effect
of housing on small business creation for tradeables and nontradeables, and
show that the effect is there for tradeables, too, alleviating concerns that the
effect is driven by local demand booms. However, our sample contains too few
manufacturing start-ups for this strategy to be implemented.

In the first two columns of Table 8, we report probit (Column (1)) and IV
(Columns (2)) estimates of model (1), where we focus on the subsample of
households living in the top half of the MSAs in SIPP, in terms of local elasticity
of housing supply. Our main result continues to hold, and in both cases, the effect
of an increase in housing wealth on new business creation is significant at the
5% statistical level. The size of the numerical effect, however, declines relative
to the full sample, suggesting that part of the increase in entrepreneurship in
response to a boom in house prices may be due to a wealth effect that operates
independently of from the home equity borrowing channel.

In Columns (3) and (4), we drop all entrepreneurs who start businesses for
which demand may have increased in the wake of a housing boom. We exclude
the following sectors: sector 3 (Construction), sector 9 (Finance, insurance,
real estate, and rental and leasing), and sector 11 (Education, health, and social

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services). We exclude the first two because entry in those may be driven directly
by an aggregate housing demand shock. As for education, health, and social
services, these are traditionally the sectors with the highest income elasticity of
demand. For example, if a housing boom makes people richer through a wealth
effect and this raises their demand for health services, then a doctor’s decision
to leave her job in a hospital and start a private practice cannot be directly
linked to the home equity borrowing channel of housing. Our results survive
the exclusion of these businesses, and the main effect is still significant at the
1% statistical level, suggesting that even though such effects are plausible, they
are not the main driver of our results in the aggregate.

Finally, the main result of the paper still obtains in the data when we combine
both strategies, that is, when we exclude households living MSAs with a very
inelastic housing supply and drop businesses which are driven by the housing
boom (Columns (5) and (6)).

3.2.3 Changes in mortgage debt after transition into entrepreneurship.
Why does higher housing wealth induce entrepreneurship? While we have

argued for a home equity borrowing channel, whereby homeowners extract
equity from their residential property to finance their business, they also simply
could be collateralizing their property to obtain business loans. One direct way
to evaluate whether housing wealth indeed drives new business creation through
a home equity borrowing channel is to observe changes in mortgage debt after
the household’s transition into entrepreneurship. If one’s house is indeed an
efficient source of wealth, a new entrepreneur will partially convert her home
equity into business investment by increasing her mortgage. If not, it would be
difficult to argue for the existence of a home equity borrowing channel. Our
data actually allow us to observe the behavior of new business owners, in terms
of changes in total and in particular in mortgage debt.

We now proceed to explicitly test for this. In practice, we estimate the
following equation:

� ln(1+mortgage debtij t +1) = β1 + β2 ·new business ownerij t
+ β3 · ln(1+mortgage debtij t ) + β4 ·Xij t
+ β5 ·�j t + εij t , (2)

where the dependent variable is the logarithm change in mortgage debt
between period t and period t + 1 for household i in MSA. A new business
owner is defined as a household head who owns or operates a business or owns
business equity at time t + 1, but did not do so at time t . Consistent with the
home equity borrowing channel hypothesis, we expect that β2 > 0. To account
for natural convergence, we include last period’s stock of mortgage debt, too.
Because we now include all individuals in the tests, and potentially do so more
than once, the sample increases to a maximum of 117,660 observations.

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Table 9
Changes in mortgage finance: Comparing business owners to nonbusiness owners

Log (1 + Home mortgage_t + 1)
− Log (1 + Home mortgage_t)

(1) (2)

New business owner 0.3092∗∗∗ 0.2793∗∗
(0.0687) (0.0883)

Log (1 + Home mortgage_t) −0.3147∗∗∗ −0.5533∗∗∗
(0.0102) (0.0126)

Individual controls Yes Yes
MSA × year fixed effects Yes Yes
Observations 117,660 77,547
R-squared 0.17 0.35

The table reports OLS estimates of the log change in mortgage debt from the previous to the current period, for
the full sample (Column (1)) and for the subsample of homeowners (Column (2)). “New business owner” is a
dummy equal to one if the household did not own business equity or operated a business in the previous period,
but does in this period, and to zero if it does not. The sample is composed of all households in SIPP for the
1996–2000, 2001–2003, and 2004–2006 waves, between the age of 22 and 60. “Home mortgage” denotes the
value of the household’s mortgage. All individual controls from Table 4 are included. Standard errors clustered
by state are reported in parentheses, where ***, **, and * indicate significance at the 1%, 5%, and 10% level,
respectively.

Table 9 reports the estimates from the regression of the change in mortgage
debt on the new business owner dummy, controlling for the same demographic
characteristics and time-varying state characteristics as in Table 4, as well as
for state and year fixed effects. We observe smaller increases in mortgage
debt for individuals whose mortgage debt is higher to begin with. Importantly,
the estimate of β2 in model (2) implies that new business owners accumulate
substantially more mortgage debt than otherwise identical individuals who did
not become entrepreneurs over the same period. This is the case both in the full
sample (Column (1)) and for the sub-sample of home owners only (Column
(2)). Taking the latter case, a new entrepreneur tends to increase her mortgage by
around 0.6 of a standard deviation more than an otherwise identical individuals
who did not become a business owner. Our results thus broadly confirm the
conjecture that financing of the business is facilitated by access to housing
wealth, in that business owners can and do use their house as a source of
equity.

4. Conclusion

In this paper, we evaluate the importance of credit constraints for new business
creation using the exogenous variation in home equity for a large sample
of U.S. homeowners over the period 1996–2006. We find that households
with higher home equity today are significantly more likely to own and
operate a business in the future. We record this result after implementing a
number of strategies aimed at isolating a variation in home equity that is
orthogonal to local economic effects. Numerically, a 10% increase in home
equity increases the probability that a non-business-owning household will
switch to entrepreneurship in the future by around 7%. This effect is robust to

2425

The Review of Financial Studies / v 28 n 8 2015

controlling for a wide range of demographic and income characteristics and for
MSA×year fixed effects. Finally, we find a strong positive correlation between
transition into entrepreneurship and the increase in mortgage debt. The intuition
is that once individuals switch from a fixed-income job to entrepreneurship, they
draw down their home equity to finance their business investment, confirming
that real estate is indeed efficient source of funds.

Our reduced-form estimates imply potentially important linkages between
housing and real economic activity. For example, our results suggest that the
housing boom before the Great Recession may have resulted in higher rates of
new business creation, while increasing levels of mortgage debt since 2006 may
have depressed self-employment, denting the positive effect on new business
creation of higher unemployment. There can be important economic spillovers
from entrepreneurship, too, such as higher growth, lower crime, or higher
rates of job creation. Regarding the latter, recent work has established that
employment disproportionately expands in industries and areas most sensitive
to changes in home prices (Adelino, Schoar, and Severino Forthcoming). In
future work, it also would be instructive to incorporate an analysis of the
implications for entrepreneurship of reducing transaction costs in the housing
market and of various exemptions related to housing in the case of personal
bankruptcy.

References

Adelino, M., A. Schoar, and F. Severino. Forthcoming. House prices, collateral, and self-employment. Journal
of Financial Economics.

Berkowitz, J., and M. White. 2004. Bankruptcy and small firms’ access to credit. RAND Journal of Economics
35:69–84.

Bernanke, B., and C. Lown. 1991. The credit crunch. Brookings Papers on Economic Activity 22:205–48.

Black, S., and P. Strahan. 2002. Entrepreneurship and bank credit availability. Journal of Finance 57:2807–33.

Blanchflower, D., and A. Oswald. 1998. What makes an entrepreneur? Journal of Labor Economics 16:26–60.

Buera, F. 2009. A dynamic model of entrepreneurship with borrowing constraints: Theory and evidence. Annals
of Finance 5:443–64.

Cagetti, M., and M. De Nardi. 2006. Entrepreneurship, frictions, and wealth. Journal of Political Economy
114:835–70.

Cetorelli, N., and P. Strahan. 2006. Finance as a barrier to entry: Bank competition and industry structure in local
U.S. markets. Journal of Finance 61:437–61.

Chaney, T., D. Sraer, and D. Thesmar. 2012. The collateral channel: How real estate shocks affect corporate
investment. American Economic Review 102:2381–409.

Chetty, R., and A. Szeidl. 2010. The Effect of housing on portfolio choice. NBER Working Papers 15998.

Corradin, S. 2014. Household leverage. Journal of Money, Credit, and Banking 46:567–613.

Corradin, S., and A. Popov. 2013. House prices, home equity, and entrepreneurship. ECB Working Paper 1544.

Cvijanovic, D. 2014. Real estate prices and firm capital structure. Review of Financial Studies 27:2690–735.

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House Prices, Home Equity Borrowing, and Entrepreneurship

Duca, J., J. Mullbauer, and A. Murphy. 2011. House prices and credit constraints: Making sense of the U.S.
experience. Economic Journal 121:533–51.

Evans, D., and B. Jovanovic. 1989. An estimated model of entrepreneurial choice under liquidity constraints.
Journal of Political Economy 97:808–27.

Evans, D., and L. Leighton. 1989. Some empirical aspects of entrepreneurship. American Economic Review
79:519–35.

Fairlie, R. 1999. The absence of the African-American owned business: An analysis of the dynamics of self-
employment. Journal of Labor Economics 17:80–108.

———. 2013. Entrepreneurship, economic conditions, and the Great Recession. Journal of Economics and
Management Strategy 22:207–31.

Hamilton, B. 2000. Does entrepreneurship pay? An empirical analysis of the returns to self-employment. Journal
of Political Economy 108:604–31.

Holtz-Eakin, D., D. Joulfaian, and H. Rosen. 1994. Sticking it out: Entrepreneurial survival and liquidity
constraints. Journal of Political Economy 102:53–75.

Hurst, E., and A. Lusardi. 2004. Liquidity constraints, household wealth, and entrepreneurship. Journal of
Political Economy 112:319–47.

Hurst, E., and F. Stafford. 2004. Home is where the equity is: Liquidity constraints, refinancing and consumption.
Journal of Money, Credit, and Banking 36:985–1014.

Kerr, W., and R. Nanda. 2009a. Democratizing entry: Banking deregulation, financing constraints, and
entrepreneurship. Journal of Financial Economics 94:124–49.

———. 2009b. Financing constraints and entrepreneurship. NBER Working Paper 15498.

Loutskina, E., and P. Strahan. 2015. Financial integration, housing, and economic volatility. Journal of Financial
Economics 115:25–41.

Mian, A., and A. Sufi. 2009. The consequences of mortgage credit expansion: Evidence from the U.S. mortgage
default crisis. Quarterly Journal of Economics 124:1449–96.

———. 2011. House prices, home equity-based borrowing, and the U.S. household leverage crisis. American
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———. 2000. Entrepreneurship, saving, and social mobility. Review of Economic Dynamics 3:1–40.

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57

3
APPRAISAL: INS AND OUTS

OF MARKET VALUE

M
arket value remains a centrally important metric (measure) to real
estate investors, mortgage lenders, and homebuyers

.

Only the
foolish would buy, sell, or loan against a property without knowl-

edge of its market value. Nevertheless, hundreds of bank failures and mil-
lions of foreclosures has laid bare the idea that this popularly relied upon
fi gure should serve as the only fi gure that matters.

Never again should anyone believe the once-entrenched view, “Buy
for less than market value, you’ve scored a good deal. Pay more than mar-
ket value, you’ve overpaid.”

Never again should anyone rely upon appraisers (without question)
to provide accurate, disinterested estimates of market value. Although
always known among real estate insiders, the market value estimates of
appraisers are not only subject to unintentional errors of fact, interpreta-
tion, or both, in many instances, appraisers manipulate their numbers to
deliver whatever value estimate makes a deal work (at least, that is, appear
to work—in the short run.)

So, this chapter will help you in two ways:

1. You will understand how to review and critique a profession-
ally prepared market value appraisal; and, correspondingly, you
will wisely decide how much confi dence you should place in the
appraiser’s estimate of value.

2. You will see that for purposes of investment, even an accurately
prepared market value appraisal fails to provide a fi gure that
offers a fair or reasonable price to pay. For that guide, forecast
future demand and supply, not rely on past sales prices.

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58 APPRAISAL: INS AND OUTS OF MARKET VALUE

WHAT IS MARKET VALUE?

To the naive, appraised value, sales price, and market value all refer to
the same idea. But, actually, appraised value might refer to an insurance
policy appraisal, a property tax appraisal, an estate tax appraisal, or a mar-
ket value appraisal. Sales price itself reveals the nominal price at which
a property has sold. That sales price might equal, exceed, or fall below
market value. Sales price represents market value only when a property is
sold according to these fi ve assumptions:

1. Buyers and sellers are typically motivated. Neither acts under
duress.

2. Buyers and sellers are well informed about the market and nego-
tiate in their own best interest.

3. The marketing period and sales promotion bring the property to
the attention of willing and able buyers.

4. No atypically favorable or unfavorable terms of fi nancing
apply. Easy money infl ates demand. Tight money suppresses
demand. (During the most recent property boom, lenders offered
dangerously easy fi nancing, thus pushing demand and sales
prices far above the market values that would have prevailed
under normal loan underwriting standards.)

5. Neither the sellers nor the buyers offer any extraordinary sales
concessions or incentives. (For example, the builders in many
countries offered off-plan buyers three years of rent guarantees—
clearly a red fl ag that the builders’ prices exceed market value.)

To illustrate the assumed conditions underlying the concept of mar-
ket value, say that two properties recently sold in a neighborhood where
you might like to invest:

The house at 37 Oak sold at a price of $258,000, and 164 Maple sold
at a price of $255,000. Each of these three-bedroom, two-bath houses was
in good condition, with around 2,100 square feet. You locate a nearby
house of similar size and features at 158 Pine. It’s priced at $234,750. Is
that a below-market price? Maybe, maybe not. Before you accept this sales
evidence, investigate the terms and conditions of the other two compara-
ble sales.

What if the sellers of 164 Maple had carried back a nothing-down, 4
percent, 30-year mortgage for their buyers (that is, favorable fi nancing)?

What if the buyers of 37 Oak had just fl own into Peoria from San
Francisco and bought the fi rst house they saw because “It was such a steal.
You couldn’t fi nd anything like it in San Francisco for less than $1.2 million”
(that is, uninformed buyers)?

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WHAT IS MARKET VALUE? 59

What if the sellers of 37 Oak had agreed to pay all of their buyer’s
closing costs and leave their authentic Chippendale buffet because it was
too big to move into their new condo in Florida (that is, extraordinary sales
concessions)? What if 37 Oak were a bank REO?

Sales Price Doesn’t Necessarily Equal Market Value

When you value property, learn more than the past prices at which so-
called similar properties have recently sold. Investigate whether the buyers
or sellers in these transactions acted with full market knowledge, negotiated
any favorable terms of fi nancing, bought (or sold) in a hurry, or conceded
something that pushed up the nominal selling price—or perhaps pulled it
down. If you fi nd that the sales of comparable properties do not meet the
conditions of a market value sale, then that sales price (unadjusted) does
not necessarily imply a market value price.

To confi dently rely on comp sales prices: verify the accuracy of your
information, verify the date of sale, and verify the terms and conditions
of the sale. Faulty information about a comp property’s features or terms of
sale can make overpriced deals look good (or vice versa). Market value
assumes no hidden defects or title issues. A comp (or subject) house with a
termite infestation or unpaid tax liens should sell at a price less than mar-
ket value (see later discussion).

Underwriting Rules Determine the Value in LTV

Banks loan against market value, not purchase price, unless your purchase
price falls below market value. When you apply for a mortgage, you may
tell the lender that you’ve agreed to a price of $200,000 and would like to
borrow $160,000 (an 80 percent LTV). Yet the lender will not necessarily
agree that this price matches the property’s market value.

The lender will ask about special fi nancing terms (for example, a
$20,000 seller second) and sales concessions (for example, the seller’s plan
to buy down your interest rate for three years and pay all closing costs). If
your transaction differs from market norms, the lender won’t lend 80 per-
cent of your $200,000 purchase price—even if it routinely does make 80 per-
cent LTV loans. The lender may fi nd that easy terms of fi nancing or sales
concessions are worth $10,000. So, the lender may calculate your 80 percent
LTV ratio against $190,000, not the $200,000 nominal purchase price.

To verify that your purchase price of $200,000 equals or exceeds mar-
ket value, the lender will order a market value appraisal. If that appraisal
report comes back with a fi gure that’s less than $200,000, the lender will

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60 APPRAISAL: INS AND OUTS OF MARKET VALUE

use the lesser amount to calculate an 80 percent LTV loan. Take notice:
Do not passively accept the results of any low (or high) appraisal. Review
and critique the report. Ask the appraiser to correct errors. Or you can ask
the lender to order a new appraisal with another fi rm. The lender needs
a fi le document (appraisal) to justify its lending decision. If you provide
an acceptable (revised or remade) appraisal of a satisfactory amount, you’ll
often get the loan you want.

Danger: Just because a lender’s appraiser comes up with a market
value estimate that matches your purchase price, never assume that the
appraisal accurately sets market value. Accept personal responsibility for
your offering price. In the past, loan reps routinely told their appraisers
the value estimate they needed to make a deal work. In return, apprais-
ers know that if they fail to hit the desired numbers, loan reps will select
another, more accommodating appraiser to prepare their reports.

If you’re a good customer of a bank (or if the bank would like you
to become a good customer), the loan rep may encourage the appraiser to
issue an MAI (made as instructed) appraisal. I know of many instances in
which appraisers have acquiesced to not-so-subtle hints from a loan rep
and submitted appraisals that overstated a property’s value. (Indeed, as
early in the property boom as 2003, government investigators found that
loan reps were pressing appraisers to lift their value estimates.)

New lender and appraisal regulations supposedly will stop this
abuse. I would not bet on it. People in the business know each other—and
word gets around. There’s no Chinese Wall in real estate. Recently, a prop-
erty that I own was reappraised so that I could refi nance and pull cash
out (to reinvest, not spend) some of that almost free money (3.75 percent
interest rate). When inspecting the house, the appraiser asked me what
I thought it was worth. His market value estimate came in a mere $5,000
less than the number I suggested. In this instance, my estimate was accu-
rate. I am not accusing the appraiser of over- or undervaluing. But it does
illustrate that he did not want to nix the loan with an out-of-range opinion.

You will work with appraisers, and you will solicit their opinions, but
never accept those opinions as the fi nal word. To protect yourself against
inaccurate appraisals (your own, as well as others), understand how to cal-
culate, apply, and interpret the three basic methods used to estimate mar-
ket value.

HOW TO ESTIMATE MARKET VALUE

To estimate market value, rely on multiple methods. During the prop-
erty and credit boom, appraisal practice focused on the comparable sales
approach and ignored (or slighted) the cost and income approaches.

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PROPERTY DESCRIPTION 61

In doing so, appraisers missed the danger signals that the cost and income
methods were fl ashing.

Cost approach
♦ Calculate how much it would cost to build a subject property at

today’s prices.
♦ Subtract accrued depreciation.
♦ Add the depreciated cost fi gure to the current value of the lot.

Comparable sales approach
♦ Compare a subject property with other similar (comp) properties

that have recently sold.
♦ Adjust the prices for each positive or negative feature of the

comps relative to the subject property.
♦ Estimate market value of the subject property from the adjusted

sales prices of the comps.
Income approach

♦ Estimate the rents you expect a property to produce.
♦ Convert net rents after expenses (net operating income) into a

capital (market) value amount. Alternatively, estimate the gross
amount of rents that a property could bring in and multiply that
amount by an applicable market-derived GRM.

When you evaluate a property from three perspectives, you check
the value estimates of each against the others. Multiple estimates and tech-
niques enhance the probability that your estimate refl ects reality. If your
three value estimates don’t reasonably match up, either your calculations
err, the fi gures you’re working with are inaccurate, or the market is acting
crazy and property prices are about to head up (or down).

Figure 3.1 shows a sample residential appraisal form for a single-
family house. Refer to this form as you read the following pages and you’ll
see how to apply these three techniques to appraise properties. Photocopy
this form (or print a copy from the Internet). Use the forms to fi ll in prop-
erty and market information as you value potential property investments.

PROPERTY DESCRIPTION

To accurately estimate the market value of a property, fi rst describe the
features of the property and its neighborhood in detail. List all facts that
might infl uence value favorably or unfavorably. Investors err in their
appraisals because they casually inspect rather than carefully detail and
compare. Focus on each of the neighborhood and property features listed

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Figure 3.1 Appraisal Report
62

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Figure 3.1 (Continued)
63

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Figure 3.1 (Continued)
64

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Figure 3.1 (Continued)
65

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Figure 3.1 (Continued)
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Figure 3.1 (Continued)
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68 APPRAISAL: INS AND OUTS OF MARKET VALUE

on an appraisal form. You will judge properties more profi tably. (Here I
focus on market value inspection and description. Valuing for investment
and entrepreneurial improvements differs in perspective and purpose. We
address those perspectives in following chapters.)

Identify the Subject Property

To identify the subject property seems straightforward. But all is not
as simple as you may think. The street address for one of my previous
homes was 73 Roble Road, Berkeley, California 94705. However, that prop-
erty does not sit in Berkeley. It is actually located in Oakland. The house
sat back from Roble Road (which is in Berkeley) about 100 feet—just far
enough to place it within the city limits of Oakland. As a result, the city
laws governing the property (zoning, building regulations, permits, rent
controls, school district, and so forth) were those of Oakland, not Berkeley.

Similarly, Park Cities (University Park and Highland Park) are high-
income, independent municipalities located within the geographic bound-
aries of Dallas, Texas. Among other amenities, Park Cities are noted for
their high-quality schools. Yet (in the past) if you lived in Park Cities on
the west side of the North Dallas Tollway, your children would attend the
lesser-regarded schools of the Dallas Independent School District.

The lesson: Street and city addresses don’t tell you what you need
to know about a property. Strange as it may seem, a property may not be
located where you think it is. The property may not receive the services you
think it does. The laws of zoning and other regulations may not apply as
you think they do. (See also the discussion of site identifi cation further on.)

Neighborhood

As the appraisal form shows, a neighborhood investigation should note the
types and condition of neighborhood properties, the percentage of houses
and condominiums that are owner occupied, vacancy rates, property price
(and rental) ranges, the types and quality of government services, and the
relative convenience of the property to shopping, schools, employment
centers, and parks and recreational areas—the appeal of the neighborhood
to potential buyers.

Next, bring the future into view. Envision the changes that are likely
to occur in the neighborhood during the coming three to fi ve years. Is the
neighborhood stable? Is it moving toward higher rates of owner occu-
pancy? Are property owners fi xing up their properties? Do neighbor-
hood residents and local merchants take pride in their properties and the

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PROPERTY DESCRIPTION 69

surrounding area? Is a neighborhood (or homeowners’) association work-
ing to improve the area? If not, could such an association make the neigh-
borhood a better place to live, shop, work, and play?

Have builders added large numbers of new housing units to the
area? Is the neighborhood fi lled with foreclosures, For Sale signs or For
Rent signs, or both? Has excess supply driven down property prices? How
many months (or years) might be needed to clear existing and pipeline
inventories?

Investment value looks to the future. Market value chiefl y looks to
past sales of similar properties. When you invest, you buy the future more
than the present. View the neighborhood with a magnifying glass and with
the use of a crystal ball. How will (or could) the neighborhood appear,
look, feel, and be made to live in within the coming 5 to 10 years?

Site (Lot) Characteristics

Depending on the neighborhood, the size and features of a lot can account
for 20 to 80 percent of a property’s current and future value. Smart inves-
tors pay as much attention to the lot (and its potential) as they do to the
building(s).

In addition to site size and features (see appraisal form), review the
rules and restrictions that govern a site. Determine whether the build-
ings conform to zoning, occupancy, environmental, and safety regula-
tions. Many two- to four-unit (and larger) properties have been modifi ed
(rehabbed, cut up, added to, repaired, renovated, rewired, reroofed, and so
on) in ways that violate current law. Laws also change. Even if the prop-
erty originally conformed to all rules and regulations, it may now violate
today’s legal standards.

Land use law classifi es properties as (1) legal and conforming, (2)
legal and nonconforming, and (3) illegal. When a property meets all of
today’s legal standards, it’s called legal and conforming. If it met past stan-
dards that don’t meet current law, but have been grandfathered, the prop-
erty qualifi es as legal but nonconforming.

If the property includes features or uses that violate standards not
grandfathered as permissible, those features or uses remain illegal. Even
work that conforms to the law might place the owners (present and future)
in jeopardy if such work was performed without a valid building or reno-
vation permit.

If you buy a property that fails to meet current law, buy with your
eyes open. Lower your offering price to refl ect risk. At some future time,
city inspectors may require you to bring the property up to code. Or if the
property suffers major fi re damage, the city may not permit a rebuild (for

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70 APPRAISAL: INS AND OUTS OF MARKET VALUE

example, a grandfathered offi ce building in a single-family-zoned district).
Health, safety, and environmental violations may present these risks:

♦ Subject your tenants to injury.
♦ Motivate a rent strike.
♦ Expose you to a lawsuit.
♦ Expose you to civil or criminal penalties (fi nes and, in serious

cases, prison).

Before you decide upon the price to pay for a property, verify code
compliance. To bring a nonconforming property up to code (or to tear out
and reinstall unpermitted work) can cost thousands (or even tens of thou-
sands) of dollars.

Improvements

After you investigate the legal restrictions relative to site size, features, and
improvements (for example, parking, driveways, fencing, landscaping,
utilities, sewage disposal), detail the size, condition, quality, and appeal
of the house or apartment units located on the site. Building size itself
ranks as one of the most important determinants of value. To determine
size (room count, square footage) requires more than pulling out a tape
measure.

As you inspect properties, you’ll see converted basements, garages,
and attics; you’ll see heated and cooled and unheated and uncooled living
areas; you’ll see bedrooms without closets and dining areas without space
for a family-size table and chairs, let alone a buffet or china cabinet; you’ll
see rooms with 6-foot ceilings or lower, and rooms with 12-foot ceilings or
higher; you’ll see some storage areas that users can access easily and others
that you can reach only by crawling on your hands and knees or standing
on a ladder. You’ll see decks, patios, and porches constructed of all sorts of
materials in all shapes and sizes.

You’ll see that all space does not live equally well. You must look
beyond measured size, purported space use, or room count. Judge quality,
livability, traffi c patterns, and functionality.

Even more challenging, not everyone measures square footage in
the same way: a builder asked fi ve appraisers to measure one of his new
homes. In sales promotion literature, the builder listed the home as 3,103
square feet. One appraiser came up with a square-footage count of 3,047
square feet. The other appraisers came up with measures that ranged
between 2,704 square feet and 3,312 square feet. Differences such as these
occur not only by mistake but because no square-footage police prescribe
or enforce measurement methods.

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THE COST APPROACH 71

When you read or hear a property’s room count or size, verify that
information. Judge the quality, size, and desirability of the space. I once
owned a lakefront house with a large master bedroom (MBR) that faced the
lake through a full, wall-sized window. In valuing that house, an appraiser
rated as equivalent another lakefront home—only its MBR was 40 percent
smaller and faced street-side.

In his report, the appraiser made no note of that huge difference
(as perceived by most would-be buyers). To compound his errors, the
appraiser also rated the lakefront lots equivalent—even though the sub-
ject’s lot was 40,000 square feet with 165 feet of frontage versus the comp
lot at 20,000 square feet with 100 feet of lake frontage. Never accept—
without verifi cation—an appraiser’s comparable data, properties, or fea-
ture adjustments. (I might note that as part of my verifi cation process,
I actually visited the owner of that comp house and he even invited me in
to look around. Throughout my career, I have often been amazed at how
accommodating and forthcoming people will become when you speak
with them in a courteous and curious manner.)

THE COST APPROACH

The cost approach recognizes that you can either build (or buy) a new
property or buy an existing one. Replacement cost typically sets the upper
limit to the price you would pay for an existing property. If you can build
a new property for $380,000 (including the cost of a lot), then why pay
$380,000 for a like-kind existing property located just down the street? In
fact, why pay $380,000 for that older property? It suffers (at least some)
physical deterioration.

Calculate Cost to Build New

To follow the logic of the cost approach, refer to the appraisal form. First,
calculate the cost to build the property using dollars per square foot. Use
a fi gure that would apply in your area for the type of property you’re
valuing. To learn these per-square-foot costs, talk with local contractors
or consult the Marshall & Swift construction cost manuals in the reference
section of your local library or on the Internet (marshallswift.com).

Because replacement costs correlate directly with the size and quality
of buildings, accurate measurement precedes accurate valuation. Notice,
too, that you add the expense of upgrades and extras (crystal chandelier,
high-grade wall-to-wall carpeting, Italian tile, granite countertops, high-end

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72 APPRAISAL: INS AND OUTS OF MARKET VALUE

appliances or plumbing fi xtures, sauna, hot tub, swimming pool, garage,
carport, patios, porches, and so on) to the cost of the basic construction.

Deduct Depreciation

After you calculate today’s building costs for the subject property, deduct
three types of depreciation: physical, functional, and external.

As a building ages, it becomes less valuable than new construc-
tion because of physical depreciation (wear and tear): The property is
exposed to time, weather, use, and abuse; it deteriorates. Frayed carpets,
faded paint, cracked plaster, rusty plumbing, and leaky roofs bring down
a property’s value when cocontrasted to new construction. What amount
of depreciation remains is your call. To fi ll in a physical depreciation fi gure
for a building in good condition, estimate, say, 10 percent or 20 percent; if
the property appears run-down, you might justify 50 percent depreciation
or greater. Or instead of applying a percentage depreciation fi gure, itemize
the costs of the repairs and renovations that would restore the property to
like-new condition.

Itemized repairs do not work as well as percentage estimates, because
you can’t economically upgrade an eight-year-old roof, four-year-old car-
peting, or a nine-year-old furnace to like-new condition. Still, one way or
another, estimate the degree to which the subject property has depreciated
relative to a newly built property of the same size, quality, and features.

Next, estimate the amount of functional depreciation. Unlike wear
and tear, which occurs naturally through use and abuse, functional depre-
ciation creates loss of value due to undesirable features such as outdated
dark wood paneling, a weirdly designed fl oor plan, low-amperage electri-
cal systems, fuse boxes, out-of-favor color schemes, or linoleum fl ooring. A
property may show little physical depreciation but still lack appeal to most
potential buyers or renters.

External (locational) depreciation occurs when a property fails to
refl ect the highest and best use for a site. You fi nd a small, well-kept house
located in an area now fi lling up with offi ces and retail stores. Zoning of
the site has changed. More than likely, the house (as a house, per se) may
not add much to the site’s value. The investor who buys the house would
likely tear it down or renovate it and create a retail store or offi ces.

For such duck-out-of-water properties, external (locational) factors
make the buildings obsolete. External depreciation can approach 100 per-
cent. With or without the building, the site should sell at approximately the
same price. This principle also applies when neighborhoods move upscale,
and well-kept three-bedroom, two-bath houses of 1,600 square feet are
torn down and replaced with 5,000-square-foot McMansions. Investors

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THE COST APPROACH 73

and builders refer to these smaller existing houses as teardowns—even
though their owners may have lovingly maintained them. The house adds
nothing to the property’s value. In fact, it diminishes value to the degree
of teardown costs and permitting fees. (Yes, you often pay a permit fee to
knock down a structure.)

Lot Value

To estimate lot value, fi nd similarly zoned (vacant) lots that have recently
sold, or lots that have sold with teardowns on them. When you compare
sites, note all features such as size, frontage, views, topography, legal
restrictions, subdivision rules, and other features that can affect the values
of the respective sites.

In the Vancouver, Canada, neighborhood where I spend summers, 40-
to 50-year-old 1,200-square-foot houses on 33-foot by 120-foot lots sell to
builders for $600,000 to $700,000. A lot with a 50-foot frontage and a tear-
down can sell for $800,000. In Vancouver’s Point Grey area, similar neigh-
borhood teardowns on 50-foot lots will easily sell for $1.2 million (perhaps
$2 million with bay or mountain views).

Although most people refer to the price increases for houses, more
often than not, the real increase occurs in the site. If you seek price increase
as a primary goal, choose your property’s site with that goal in mind (see
Chapter 4).

Estimate Market Value (Cost Approach)

As you can see on the appraisal form, after you complete these steps
( calculate a property’s construction cost as if newly built, deduct depreci-
ation, and add in site value), you have computed market value. Because
you can’t precisely measure construction costs, depreciation, or site value,
the cost approach won’t give you a perfect answer (of course, neither do
the comparable sale or income approaches—reason and judgment rule).
But the cost approach does provide a reference point to use with the comp
sales and income approaches. It also helps to reveal when a market (or
property) is overvalued—priced beyond reason—or perhaps priced below
investment value.

Here’s an example of the cost approach:
Property description: Six-year-old, good-condition, single-family

house of 2,200 square feet. The house includes a two-car, 500-square-foot
garage, a deck, in-ground pool, sprinkler system, and premium carpets,
appliances, and kitchen cabinets. Nearby vacant lots have recently sold
for $60,000.

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Eldred, Gary W.. Investing in Real Estate, John Wiley & Sons, Incorporated, 2012. ProQuest Ebook Central,
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74 APPRAISAL: INS AND OUTS OF MARKET VALUE

Dwelling (2,200 × $108 per-square-foot base
construction costs)

$237,600

Upgrades 13,500
Deck, lap pool, sprinklers 21,750
Garage (500 × $33 per square foot) 16,500
Total $289,350

Less  
Physical depreciation at 10 percent (28,935)
Functional depreciation at 5 percent (14,438)
Depreciated building value $245,978
Site improvements (sidewalks, driveway,
fencing, landscaping)

18,750

Lot value 60,000

Equals  
Indicated market value, cost approach $324,728

Builders typically build only when they think they can construct
properties that will sell (or rent) to yield enough revenue to cover their
construction costs and a competitively determined profi t margin. You
can usually expect market prices of existing properties to increase when
construction costs for newly built houses signifi cantly exceed the market
values of those houses (or apartments).

Why? Because without the expectation of profi t, builders stop building.
When growing demand begins to push against a scarce supply, builder profi t
margins eventually return. The real estate construction cycle starts anew.

When builder profi ts fatten, sooner or later, they overbuild. High,
expected profi ts lead to a surplus of new construction. Too much housing
inventory brings down market values for new as well as existing properties.

Did I hear someone say Las Vegas, Miami, Dublin, or Dubai? Easy
fi nancing encouraged buyers to pay prices that (temporarily) supported
infl ated builder profi t margins. Builders overbuilt. Buyers overpaid and
overleveraged. Indeed, they overpaid because they could borrow excessive
amounts with few qualifying standards.

During the boom, the house valued at $324,728 by the cost approach
example would likely have sold for $550,000 to $600,000. The cost to build
was far less than the prices at which older properties were selling—a sure
sign that home prices had reached unsustainable amounts. But many
appraisers paid little attention because they were being paid so well to jus-
tify such excessive prices.

Investors rejoice. Overbuilding leads to underbuilding. During
the current downturn, new housing starts have nosedived to fewer than

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THE COMPARABLE SALES APPROACH 75

400,000 units—down from 1,600,000 units in 2006. Only large price gains
will bring builders back into the game. Until market values signifi cantly
increase, homebuilders will not build many new houses (and even fewer
condominiums—as their prices in many cities have fallen even more than
single-family residences). As new construction sinks—and as inventories of
foreclosures and REOs are eventually sold—the market generates the con-
ditions to support the next cyclical upswing.

THE COMPARABLE SALES APPROACH

For houses, condominiums, co-ops, townhouses, and apartment buildings,
the comparable sales approach often provides a good estimate of market
value. Understand the recent sales prices, terms of sale, physical features,
and locational differences of similar properties. Even income approaches
require knowledge of comp sales to derive market rent levels, GRMs, and
capitalization (cap) rates.

Select Comparable Properties

To apply the comp sales approach, the appraiser (or you) fi nds recently
sold properties that closely match a subject property. Ideally, fi nd comp
sales that resemble one another in property size, age, features, condition,
quality of construction, room count, fl oor plan, and location. As a practical
matter, you seldom fi nd perfect comp matches because each property, each
location, displays unique characteristics.

But you don’t need a perfect match. When you fi nd comp sales that
appear to be similar to a subject property, ballpark a market value estimate:
compare price per square foot of living area.

You research three sales: (Comp 1) 1,680 square feet, (Comp 2) 1,840
square feet, and (Comp 3) 1,730 square feet. These properties sold recently
for the respective prices of $225,120, $213,440, and $211,060. To fi gure the
selling price per square foot of living area for these houses, divide the sales
price of each house by its total square footage.

Comp 1
$225,120/1,680 � $134

Comp 2
$213,440/1,840 � $116

Comp 3
$211,060/1,730 � $122

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76 APPRAISAL: INS AND OUTS OF MARKET VALUE

If the house you’re interested in has 1,796 square feet of living area, it
will probably sell in the range of $120 to $130 per square foot, or $215,520
to $233,480.

Approximate Value Range—Subject Property

$120 × 1,796 = $215,520
$130 × 1,796 = $233,480

Sales price per square foot provides a fi rst-pass estimate. To gain
more insight, compare and contrast similar properties to your subject prop-
erty on a feature-by-feature basis. You adjust the sales price for each of the
comps—up or down—depending on whether its features look inferior or
superior to the subject property.

Adjust for Differences

Here’s a brief example of this adjustment process:

Adjustment Process (Selected Features Only)

  Comp 1 Comp 2 Comp 3

Sales price $225,120 $213,440 $211,060
Features      
Sales concessions 0 −10,000 0
Financing concessions −15,000 0 0
Date of sale 0 10,000 0
Location 0 0 −20,000
Floor plan 0 5,000 0
Garage 11,000 0 17,000
Pool, patio, deck −9,000 −13,000 0
Indicated value of subject $212,120 $205,440 $208,060

As you adjust the selling prices of similar houses, you move toward
your best estimate of the market value range for the subject property.
Whereas price-per-square-foot indicated a market value range for the subject

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THE COMPARABLE SALES APPROACH 77

property between $215,520 and $233,480, after adjustments, a price range
between $212,120 and $205,440 seems closer.

Explain the Adjustments

To adjust for differences in size, quality, or features, equalize a subject
property and each of its comparables: “At what price would the compa-
rable have sold if it exactly matched the subject property?” Look at the
$15,000 adjustment to Comp 1 for fi nancing concessions.

In this sale, the sellers carried back a 90 percent LTV mortgage
(10 percent down) on the property at an interest rate of 5.5 percent. At
the time, investor fi nancing usually required a 75 percent LTV (25 per-
cent down) and a 6.50 percent interest rate. Without this favorable owner
fi nancing, Comp 1 would probably have sold for $15,000 less than its
actual sales price of $225,120. Remember, favorable terms of fi nancing
often gain sellers a bonus in price. Because the defi nition of market value
assumes fi nancing on terms typically available in the market, the sales
price premium generated by this OWC (owner will carry) fi nancing is sub-
tracted from Comp 1’s actual selling price. Here are the explanations for
other adjustments:

Comp 1 garage at (+) $11,000. The subject property stands superior
with its oversize double-car garage, whereas Comp 1 has only a
single-car garage. With a larger garage like the subject’s, Comp 1
would have brought an $11,000 higher sales price.

Comp 1 pool, patio, and deck at (–) $9,000. Comp 1 is superior to the
subject property on this feature because the subject lacks a deck
and tile patio. Without this feature, Comp 1 would have sold for
$9,000 less.

Comp 2 sales concession at (–) $10,000. The $213,440 sales price in this
transaction included the seller’s custom-made drapes, a washer
and dryer, and a backyard storage shed. Because these items aren’t
customary in this market, the sales price is adjusted downward to
equalize this feature with the subject property, whose sale will not
include these items.

Comp 2 fl oor plan at (+) $5,000. Unlike the subject property, Comp 2
lacked convenient access from the garage to the kitchen. The
garage was built under the house; residents must carry groceries
up an outside stairway to enter the kitchen. With more conven-
tional and convenient access, the selling price of Comp 2 would
probably have increased by $5,000.

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78 APPRAISAL: INS AND OUTS OF MARKET VALUE

Comp 3 location at (–) $20,000. Comp 3 was located on a cul de sac, and
its backyard bordered an environmentally protected wooded area.
In contrast, the subject property sits on a typical subdivision street,
and its rear yard abuts that of a neighbor. Because of its less favor-
able location (heavier traffi c four-lane street), the subject property
could be expected to sell for $20,000 less than Comp 3.

Now, you might ask: “How can I or anyone else come up with accu-
rate amounts for each of these adjustments?” Sorry, there’s no easy answer.
Stock up knowledge by talking with sales agents and tracking sales
transactions over a period of months and years. Or, use the PFA technique
that many appraisers rely on. What’s PFA? Pulled from the air. That’s why
appraisers offer opinions of market value—not defi nitive answers.

Even without professional (or PFA) knowledge, critique the opinions
of appraisers and real estate agents against your own judgment. Ask
questions. Explore their reasoning. Verify their facts. As you look at
properties, discipline your mind to list and detail all features that make
a difference. Before you attach adjustment numbers to each property’s
unique features, fi rst observe those differences.

[Investor Alert: Properties with similar features make the best comps
for purposes of market value. As far as an appraiser is concerned, the more
closely the comps mirror the subject, the easier the market value appraisal.
Your discovery process should also look elsewhere. As an investor, you seek
to differentiate your property from others. Search out those differences that
make a difference. What unique features provide the WOW! appeal that
attracts and retains tenants? Discover what few appraisers ever look for:
those differences that deliver a compelling value proposition to prospec-
tive tenants. For market value appraisals, unique differences add diffi culty
to the appraisal task. For entrepreneurial investors, unique differences can
create value.]

THE GRM INCOME APPROACH

Near the bottom of page 3 on the appraisal form, notice a line labeled
“Indicated Value by Income Approach (If Applicable).” This income
approach refers to the gross rent multiplier (GRM). To calculate market value
using the GRM, fi nd the monthly rents and sales prices of similar houses (or
apartment buildings).

Say you discover the following single-family rental houses: (1) 214
Jackson rents for $1,045 a month and sold for $148,200; (2) 312 Lincoln
rents for $963 a month and sold for $156,000; and (3) 107 Adams rents for

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THE GRM INCOME APPROACH 79

$1,155 a month and sold for $168,400. With this information, you calculate
a range of GRMs for rental houses in this neighborhood:

GRM � Sales price/Monthly rent

Property Sales Price Monthly Rent GRM

214 Jackson $148,200 ÷ $1,045 = 142
312 Lincoln 156,000 ÷ 963 = 162
107 Adams 168,400 ÷ 1,170 = 144

If the house you value could rent for $1,000 a month, calculate a
value range using the GRMs indicated by these other neighborhood rental
houses:

GRM   Monthly Rent Value

142 × $1,000 = $142,000
162 × 1,000 = 162,000
144 × 1,000 = 144,000

Thus, the value ranges between $142,000 and $162,000.
The GRM method does not directly adjust for sales incentives,

fi nancing concessions, features, location, property condition, or property
operating expenses. This technique yields a rough estimate of market value.
Nevertheless, for property investors, it works as a rule of thumb. As with
the comp sales approach, the GRM derives from similar properties in the
same neighborhood.

For apartment buildings, the GRM is calculated from annual rent col-
lections rather than monthly. For example:

Multiunit Income Properties

Property Sales Price Total Annual Rents GRM

2112 Pope (fourplex) $280,000 ÷ $35,897 = 7.8
1806 Laurel (sixplex) 412,000 ÷ 56,438 = 7.3
1409 Abbot (sixplex) 367,000 ÷ 53,188 = 6.9

The GRMs shown in these examples do not necessarily correspond
to the GRMs that apply in your city. Even within the same city, neigh-
borhoods differ in their GRMs. During the boom within the San Diego

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80 APPRAISAL: INS AND OUTS OF MARKET VALUE

area, GRMs for single-family houses in La Jolla exceeded 400; in nearby
Claremont, you could fi nd GRMs in the 250-to-300 range. Within the same
market area, GRMs for single-family houses typically range higher than
those of condominiums. In San Francisco, small multiunit buildings sold
for annual GRMs of 14 or higher.

Did such historically high GRMs in San Francisco and San Diego sig-
nal overvalued? Absolutely! With such high price and rent multiples, rent-
ing cost 50 to 65 percent less than owning. Financially, buying made sense
only if you assumed that home prices would continue climbing higher and
higher, that is, speculators would continue to buy—no matter how high the
price. (Income investors had long before dropped out of the bidding—as
per John Burr Williams, p. 30.)

Today in Detroit, Michigan, I have seen annual GRMs of less than 4.
Has the market overreacted to Detroit’s economic problems? In my area
of Florida, I am buying well-kept and appealing properties with annual
GRMs of 6 to 8—down from the 10 to 12 GRMs of six years ago.

Unlike Detroit, whose price drops relate to a 40 percent decrease
in population and jobs (economic base), the Florida population and job
base will continue to grow (especially as the retiring boomers relocate to
warmer, higher-quality-of-life Sunbelt states). With construction of new
homes in Florida at post–World War II lows, patient investors can wait for
inventories of foreclosures to become absorbed. Price gains are virtually
assured. Fortunately, too, positive cash fl ows make that wait profi table.

INCOME CAPITALIZATION

To value apartment buildings, investors also use direct capitalization:

V = NOI/R

V represents market value. NOI represents the net operating income
of the property. R represents the overall rate of return on capital that buy-
ers of similar investment properties require.

Net Operating Income

Net operating income equals the annual gross potential rental income
from a property less expenses (vacancy and collection losses, operating
expenses, replacement reserves, property taxes, and property and liability
insurance). Look through this net income statement for an eight-unit apart-
ment building. Each unit rents for $725 a month:

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INCOME CAPITALIZATION 81

Income Statement (Annual)

1. Gross annual potential rents ($725 × 8 × 12) $69,600
2. Income from parking and storage areas 6,750
3. Vacancy and collection losses at 7% (5,345)
4. Effective gross income $71,005

Less operating and fi xed expenses 
5. Trash pickup $1,440
6. Utilities 600
7. Registration fee 275
8. Advertising and promotion 1,200
9. Management fees at 6% 4,260
10. Maintenance and repairs 4,000
11. Yard care 650
12. Miscellaneous 3,000
13. Property taxes 4,270
14. Property and liability insurance 1,690
15. Reserves for replacement 2,500

Total operating and fi xed expenses $23,885
16. Net operating income (NOI) $47,120

The following list explains each of the NOI statement entries:

1. Gross annual potential rents. The largest possible sum of rents that
you could collect at market rent levels and 100 percent occupancy.

2. Income from parking and storage areas. This property has a 16-car
parking lot. A shortage of on-street and off-street parking in the
neighborhood makes it profi table for the owner to rent the park-
ing spaces independently of the apartment units. Also, the owner
built storage bins in the basement of the building that are available
for rental to tenants.

3. Vacancy and collection losses. Market vacancy rates in the area
range between 5 and 10 percent. All units in this building are
rented. But even the best-managed apartments experience some
vacancies when apartments turn over. Add in some losses for ten-
ants who disappear owing rents that exceed the amounts of their
security deposits.

4. Effective gross income. The amount of cash that an owner receives
net of vacancy and collection, but before operating, fi xed, and
fi nancing expenses.

5. Trash pickup. Self-explanatory.
6. Utilities. Tenants pay their own unit utilities. The property owner

pays for lighting in the hallways, basement, and parking area.

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82 APPRAISAL: INS AND OUTS OF MARKET VALUE

7. Licenses and permit fees. Apartment building owners must some-
times pay for business licenses and other fees. For this property,
the owner pays a rental property registration fee.

8. Advertising and promotion. These units rent by word of mouth,
craigslist.org, or a For Rent sign that’s posted on the property. To
be safe, however, an advertising and promotion expense of $150
per year per unit is allocated to the operating budget.

9. Management fees. The owner of this apartment building self-
manages the property. Nevertheless, he should pay himself the
same amount he would otherwise have to pay a property man-
agement fi rm. Labor deserves pay and is distinct from return on
investment. Do not reward the seller for the work that you will
contribute to the property. Count self-management as an expense.

10. Maintenance and repairs. The current owner and her husband
clean, paint, and make small repairs around the property. These
labors deserve payment from the property’s rent collections.

11. Yard care. The owner pays this amount to one of the tenants to
keep the grass cut, rake leaves, and shovel snow off the walks.

12. Miscellaneous. This expense covers legal fees, supplies, snow
removal from the parking lot, municipal assessments, auto mile-
age to and from the property, and other items not accounted for
elsewhere in the income statement.

13. Property taxes. City, county, and state taxes annually assessed
against the property. Beware: Tax assessors periodically revalue
properties to refl ect increases in market prices. Future tax bills
could jump 30 to 40 percent over the amount of the previous tax
years. Similarly, if your purchase price comes in less than the
assessor’s current assessed value, request that the assessor reduce
your taxes (see Chapter 14).

14. Property and liability insurance. This insurance reimburses for
property damage caused by fi re, hail, windstorms, sinkholes,
hurricanes, and other perils. It also pays to defend against, and
compensate for, lawsuits alleging owner negligence (for example,
slip-and-fall cases).

15. Reserves for replacement. Building components wear out. The roof,
plumbing, appliances, and carpeting must be replaced periodi-
cally. Average these costs on a per-year basis.

16. Net operating income (NOI). Total all operating expenses and sub-
tract this sum from the effective gross income. The resulting fi g-
ure equals net operating income (NOI).

When you calculate NOI, include all expenses for the coming year.
Never accept a seller’s income statement as accurate. Sellers notoriously

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INCOME CAPITALIZATION 83

omit and underestimate expenses. (Corporate CEOs aren’t the only ones
who try to dress up their numbers to paint a pretty picture.)

Ask to see the seller’s tax return IRS Schedule E for the subject prop-
erty. The truth will probably sit somewhere between the owner-prepared
income statement for sales purposes (where income is likely to be over-
stated and expenses understated) and a tax return (on which some own-
ers understate income and overstate expenses). Even if the seller truthfully
reports the most recent past year’s income and expenses, estimate how
each of those amounts might increase (or decrease) in the coming years.
You buy the future, not the past.

Are property tax assessments headed up? Are vacancy rates (or
rent concessions) increasing? Have utility companies scheduled any rate
increases? Has the seller deferred maintenance on the property? Has the
owner allocated suffi cient amounts for replacement reserves? Has the seller
self-managed or self-maintained the property and therefore failed to include
his unpaid managerial and maintenance work as cash expenses? When
calculating NOI, accept no numbers on faith. Savvy investors reconstruct
seller-prepared NOIs.

Estimate Capitalization Rates (R)

You pay now for the rents the property will produce over the next 20, 30, or
40 years (more or less). The question becomes how much these future rents
are worth in today’s dollars (that is, the property’s market value). If the
appropriate capitalization (cap) rate is 8.5 percent, then the market (capital)
value of this eight-unit apartment building equals $554,365:

$47,121 (NOI)/.085 (R) � $554,365 (V)

But where does that .085 percent cap rate come from? You estimate
it from the cap rates that other investors have applied to buy similar
properties. Say a real estate agent provides you NOI and sales price data
on four similar apartment buildings that recently sold:

Market Data

Comparable Property Sales Price NOI R

Hampton Apts. (8 units) $533,469 $43,211 .081%
Woodruff Apts. (6 units) 427,381 35,900 .084
Adams Manor (12 units) 694,505 63,200 .091
Newport Apts. (9 units) 671,241 53,700 .080
Subject (8 units) (estimated) 554,365 47,121 .085

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84 APPRAISAL: INS AND OUTS OF MARKET VALUE

From these data, calculate a market-derived cap rate for each prop-
erty (after verifying that each sale meets the criteria of a market value
transaction). When investors in this area buy small income properties
similar to the subject property, they fi gure cap rates between 8.1 and 9.1
percent. So it appears that the market of comp sales indicates a cap rate of
around 8.5 percent for the subject property.

Compare Cap Rates

In your market, you may not discover suffi ciently similar properties with
such a narrow range of cap rates. You might fi nd that some apartment
buildings have recently sold with cap rates of 5 to 6 percent (or lower)
and others have sold with cap rates of 8 to 9 percent (or higher). Why such
differences?

You pay for a quantity of future rental income, and you pay for the
quality of that income. Today’s price also incorporates expectations about
the future price or income gains for that property. The greater its expected
rate of appreciation, the higher the price you pay now. Therefore, the
higher the quality of the income stream, and the larger the expected gain
in price, the lower the capitalization rate (or, conversely, the lower the qual-
ity of the property’s income and price gain potential—in the eyes of the
market—the higher its cap rate).

To illustrate: You compare two fourplexes. Santa Fe Villas is a relatively
new property located in a well-kept neighborhood near a city’s growth cor-
ridor. Several nearby offi ce towers are under construction. Dumpster Manor
is located in a deteriorating part of town. Major employers have moved out,
closed, or laid off workers. Crime rates are high and moving higher. Two
recent drug-related murders made front-page news.

If the annual NOIs for these two fourplexes are, respectively, $24,960
and $12,480, how much would investors pay for each property? If investors
applied a 10 percent cap rate to each property’s income stream, they would
value the properties as follows:

a. Santa Fe Villas
$24,960 (NOI)/.10 (R) � $249,600 (V)
b. Dumpster Manor
$12,480 (NOI)/.10 (R) � $124,800 (V)

But in the real world, investors would not apply the same cap rate
to these very unlike properties and neighborhoods. The quality of their
income streams differs. Santa Fe Villas offers more stable rents, safe neigh-
borhood, greater convenience to good jobs, and market-expected price

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INCOME CAPITALIZATION 85

growth. Investors might actually capitalize the respective NOIs of these
two fourplexes at rates of, say, 6 percent for Santa Fe Villas and 15 percent
for Dumpster Manor.

Investors would rather own a property in a prospering area. They
will pay more for each dollar of income produced by such a property.
But more is not really the issue. The real issue is, how much higher
price is justified for the better properties? When market expectations
run ahead of reasoned analysis, prices overshoot their real potential for
price and income growth. Likewise, a surfeit of bad news and pessimis-
tic expectations can drive prices below (and, correspondingly, cap rates
above) the actual risk-and-reward prospects of a disdained property.

Relative Prices: The Paradox of Risk

and Appreciation (Depreciation)

Odd as it sounds, higher-priced seemingly low-risk–high-appreciation
properties may actually produce more risk and slower gains in price (or
even more rapid declines in price) than their low-rent, highly troubled
cousins who are located on the wrong side of the railroad tracks. That’s
why many investors now buy rental houses in Detroit.

Consider this stock market analogy. If you could buy a quality,
high-growth company’s stock at a price-to-earnings ratio (P/E) of 10 or
a low-growth company’s stock at a P/E of 10, by all means invest in the
high-growth company. If you could buy a low-risk, high- appreciation-
potential property with a cap rate of 10 percent or a higher-risk, lower-
expected-appreciation property with a cap rate of 10 percent, buy the
low-risk, high-appreciation property. However, that’s not how markets
price either real estate or fi nancial investments.1 In the real world, inves-
tors bid up prices for high-quality, growth-area properties and reduce their
bids for so-called high-risk properties in less desirable neighborhoods. To
fi gure out which type of property and location offers the most profi t poten-
tial, compare their relative prices, cash fl ows, amortization, and 19 other
potential sources of return (see p. xxvi).

When investors optimistically bid up the prices of some properties,
neighborhoods, and cities relative to other properties, neighborhoods, and
cities, you can profi tably redirect your investment strategy (or even with-
draw and wait for good sense to return to the market—after the crash). In

1If you bought Microsoft and JCPenney stock in 1998 and sold in 2004, JCPenney stock
would have paid you higher returns. As a high-profi le growth company, in 1998,
Microsoft’s stock included a too-hefty price premium for its expected growth.

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86 APPRAISAL: INS AND OUTS OF MARKET VALUE

other words, don’t calculate market cap rates for just one type of property
or neighborhood. Learn as much as you can about a variety of submarkets
and areas of the country.

You overpay for a property when: (1) you apply a cap rate that’s too
low relative to the property and neighborhood you’re buying; or (2) you do
not detect that market cap rates themselves may sit too low relative to cash
fl ows, other types of properties, other locations, or even other available
investments. In some areas during the boom, cap rates for rental houses
fell as low as 3 to 4 percent—well below their historical level of, say, 5 to 10
percent. And well below the level necessary to generate positive cash fl ows
(after paying debt service).

VALUATION METHODS: SUMMING UP

Market value does not necessarily equal appraised value or sale price.
Market value refers to the sale price of a property when a sale meets the
criteria of a market value transaction. To estimate the market value of
a subject property as it compares with other similar properties that have
sold, fi rst investigate the terms and conditions under which the compara-
tive properties sold. A property down the street that sold for $600,000 after
just three days on the market does not necessarily indicate that a similar
property nearby will sell for $600,000. It depends on the terms of sale and
the detailed features of each property.

You can apply at least three approaches to estimate the market value
of a property, but those three approaches do not necessarily produce the
same number. Investors and appraisers rely on imperfect and incomplete
data. Decide which approaches best serves your purposes. The accuracy of
your market value estimate directly relates to how well you identify and
evaluate a property’s features. Observe the differences (positive or nega-
tive) that make a difference. Wise investment decisions require you to iden-
tify and understand features, properties, neighborhoods, construction
costs, and lot values. Technique never substitutes for knowledge, close rea-
soning, and well-informed (but rarely perfect) judgment.

Past price increases (or decreases) do not forecast the future. Market
value itself does not warn against buying a property that’s about to fall in
price. And if you buy at less than market value, you have not necessarily
chalked up a bargain. You can make great returns—even when you pay
market value (or above)—if you have identifi ed a property (or location)
that’s about to gain increased popularity or identify a property in which
you envision spectacular ways to create value. (Of course, some property
investments combine all of these advantages and more: lumps of coal are
transformed over time into diamonds.)

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VALUATION METHODS: SUMMING UP 87

Appraisal Limiting Conditions

Property appraisers hedge their estimates of value with limiting condi-
tions. Especially relevant (Figure 3.1) are limitations 1, 2, 6, and 7.

♦ Appraisers do not investigate title. They assume that a property’s
bundle of fee simple rights is good and marketable. For a legal
guarantee of property rights, consult a title insurance company.

♦ Appraisers do not survey the boundaries of a site, nor do they nec-
essarily note encroachments or other potential site problems. To
precisely identify site dimensions, encroachments, and easements,
employ a surveyor and walk the property lines.

♦ Appraisers examine through casual inspection. To thoroughly assess
the soundness of a property and its systems (heating, cooling, elec-
trical, plumbing), hire a competent building inspector and skilled
tradespersons.

♦ Appraisers gather much of their market information from second-
hand sources (real estate agents, government records, mortgage
lenders, and others). Appraisers seldom go inside the comp proper-
ties that they include in their appraisal reports. Because they incor-
porate unverifi ed secondhand data, appraisals often err in fact and
interpretation. Accept an appraisal report as for-what-it’s-worth
information. Never weight it more than reason warrants. (As noted,
I always verify the appraiser’s comp property data before I decide
how much respect I should give to an appraiser’s estimate of value.)

Valuation versus Investment Analysis

Before you buy, understand the property’s market value. Yet market value
does not inform suffi ciently. Besides fi guring out a best guess of market
price (or, more accurately, a price range), answer these questions:

♦ Will the property generate adequate cash fl ows?
♦ Can you expect the property to increase in price?
♦ Can you add value to the property?

To address these investment issues, we turn to the following chapters.

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