MIT Center for Real Estate Week 9: Housing...

enter for Real EMIT C state

Week 9: Housing Markets• Sales, mobility and turnover: the market for for

housing services. • Vacancy, sales time and prices: the “large” impact

of small net changes. • The net demand for housing. • The full annual cost of housing ownership:

consumption and investment motives. • Housing demand “bubbles”. • New Development and the behavior of housing

supply.

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Gross annual flows in the US housing market (1989)

Population: 248m

Renters: 33.7m HH 60m HH

Annual Growth: .94%

Annual Growth: 1.46%

Household size: 2.65

8.8m HH 2.9m HH

2.2m HH

2.3m HH

MIT C state

Households: 93.7m

Owners:


As income increases so does housing expenditure: what is the income elasticity (∂E/E)/(∂y/y)?

Average Value of Home Owned by Married Couples As a Function of Income, 1989 AHS

Age of Head of Household

With Children Without Children

Household Income 25-34 35-44 45-54 55-64 65+

Less than $20,000 65,40770,81743,822 81,51472,928

$20,000 - $29,999 77,35373,20651,145 100,75076,427

$30,000 - $39,999 77,72075,58861,964 101,464*87,030*

$40,000 - $49,999 111,97598,54493,814 113,643*102,495*

$50,000 - $74,999 114,804122,282109,679 152,532*117,287

$75,000+ 196,848190,244182,377 160,292*171,571

adapted from DiPasquale and Wheaton (1996)

Values reported by home owners

*small sample size


Is there a housing consumption elasticity with respect to household size?

Average House Value for Homeowners by Income and Household Size for Households with Head Aged 35-44, 1989 AHS

Household Size

Income 1 Person 2 People 3-4 People 5+ People All

$60,000+

$40,000 - $49,999

$40,000 - $59,999

$25,000 - $39,999

Less than $25,000

107,519111,307109,993104,78783,840

163,023165,728162,889161,205150,791

106,247107,873104,897106,365113,421

77,86881,56475,59980,36579,327

60,64857,51669,84051,43852,506


Values reported by home owners

enter for Real EHousing Tenure: Younger households and poorer

MIT C state

households are most likely to rent. Is renting a “lifestyle choice” or are some “constrained” to rent?

Homeownership Rates by Age and Income, 1990 CPS

Income (thousands)

Age of Head of Household <20 20-29 30-39 40-49 50+ All Incomes

All Ages

65+

45-64

35-44

25-34

58.348.3

84.967.5

73.159.4

55.236.6

37.3% 21.7%

64.184.374.968.0

75.591.789.687.6

78.190.585.681.5

66.585.477.668.3

44.3% 68.5% 58.9% 53.4%



Most households move because the current home or location they live in has become “inadequate”.

Reasons for Moving, 1989*, AHS

% of Total Responses**

Total Owner Renter

Housing related reasons 42.356.446.4

Job related reasons 24.315.121.6

Family changes (marriage, divorce, etc.) 16.214.115.6

Miscellaneous other 11.311.411.3

Displacement by government or private sector 5.4 2.7 4.6

Disaster loss (fire, flood, etc.) 0.5 0.3 0.4

* Reasons for moving cited by households who had moved within the last 12 months. ** Respondents could cite more than one category.


enter for Real ERenters Move More:

MIT C state Lower Transaction costs, less

maintenance… Older people move less: because they own, or do they own because they move less?

Mobility Rates* by Age and Tenure, 1989, AHS

Tenure %

Age Owner Renter All

Under 25 49.956.824.6

24-34 33.444.618.7

34-44 16.732.88.5

45-54 11.328.45.7

55-64 7.2 19.64.0

65+ 4.6 12.22.2

All 17.835.77.6

•Heads of household in each category who had moved withing the last 12 months, as a percent of total households per category. •adapted from DiPasquale and Wheaton (1996)

MIT Center for Real Estate

Vacancy = a spell (length of time) Rental:

Vacancy rate = Incidence rate x Duration Incidence rate = % of units loosing tenant

per month. Duration = # months necessary to lease up

Owner:Average Sales time = Vacant Inventory/

Sales (units/month) [or # movers]

[Empirics: see Gabriel-Nothaft]


The relative role of Incidence and Duration: which changes most across time, across markets?


Are there “structural” differences in vacancy, mobility and sales time between markets?

Homeowner Vacancy and Mobility Rates by Metropolitan Area, 1989 AHS

Annual Mobility Vacancy Rate Rate Ratio (years to sale)

Minneapolis/St.Paul 0.6 8.9 0.067

Los Angeles 0.9 9.1 0.099

San Francisco 0.9 8.6 0.104

Detroit 1.0 6.9 0.145

Boston 1.0 5.5 0.181

Washington, D.C. 1.1 10.6 0.104

Philadelphia 1.2 5.8 0.208

Phoenix 2.8 12.0 0.234

Dallas 3.9 10.1 0.386


enter for Real EMIT C state How owners transition from one house to another. The risk of owning two homes (bridge financing).What happens when there is no such mechanism?

Matched Household One house

Mismatched Household Searching for home

Matched Household

Household “change”

Sale of previous home

Owning two homes

Successful search


Buyer strategy: once purchased, will have to own a second home. Maximum Buyer Offer would be such that this cost

negated the advantage of move

L: expected sales timei : interest rate, opportunity cost of time iLP: holding cost of owning 2nd home

during sale process. Buyer Max Offer (BMO): BMO x iL = Net gain from moving. BMO = Net gain/ iL


Seller strategy: What minimum (certain) price would be as profitable as putting the house back on the market and

taking a gamble on a price of P.

Seller Min. Accept (SMA) = P/ (1+ iL)

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Bargaining theory: Negotiated price lies between BMO and SMA, assuming that BMO>SMA

MIT C state

P = BMO - ½[BMO – SMA] (Solving for P – which is also part of the formula for SMA)

= Net Gain x 1 + iL iL 1 + 2iL

Outcome: price moves almost inversely to sales time (and vacancy).


Since Vacancy moves prices, how do we measure, model or forecast Vacancy?

1). Vacancy = stock – occupied units 2). Occupied units = “ex post demand” (after price clears the

market) = # of “households” 3). Housing “demand” ex ante = # of potential households. 4). Ex ante demand is the full demand curve. Ex post is that

point on the curve that represents current consumption. 4). Changes in vacancy come from new construction (adds to

stock) and changes in ex ante demand (that then affects ex post demand).

5). If demand (ex ante) and supply move together, then vacancy is relatively flat at its “structural” rate.

enter for Real EMIT C state Residential vacancy rates move remarkably little (in

comparison to commercial. Supply seems quite disciplined relative to demand. Yet there are significant swings in

rents and house prices!

0

1

2

3

4

5

6

7

8

9

10

11

1968

.119

69.1

1970

.1

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1972

.119

73.1

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.119

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1976

.119

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1978

.119

79.1

1980

.119

81.1

1982

.119

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1984

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85.1

1986

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1990

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.120

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l ( ) i - ily ( i ) i le - il i i )

U S R e n ta l M u lti-F a m ily v s . H o m e o w n e r V a c a n c y R a te s

R e n ta Mu lti-F a m ily 2 o r M o re U n its in S tru c tu re Hom e ow n e r S ng le F a m 1 Un it n S tru c tu re H o m e o w n e r S n g F a m y (1 o r Mo re U n ts n S tru c tu re


U.S. Single-Family Market:Completions move with the economy, and

home prices adjust quickly. 1980-81 1990-91 2001-02

7%

6%

5%

4%

3%

2%

1%

0%

-1%

-2%

-3%

-4%

-5%

1976

1977

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

Completions Rate Employment Growth Home Price Index (2002 $)


U.S. Multi Family Market: Supply not as closely aligned to Demand. $ Per Sqft

6.00% 750.00

Forecast

5.00%

700.00 4.00%

3.00% 650.00

2.00%

600.00 1.00%

0.00% 550.00

-1.00%

-2.00% 500.00

1971

1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

Total Employment Growth (L) Real Rent (R) Completion Rate (L)


Theories of prices (or rents) and Vacancy?

1). If vacancy is always “constant”, at some “structural” rate V*, price must be adjusting quickly so that ex ante = ex post = stock(1-V*). Implication: ex ante and ex post “demand” are difficult to distinguish. This is the “structural” theory of Vacancy

2). With large systematic vacancy movements, prices or rents must be “sticky” and not adjusting quickly. Implication: ex ante can be measured and distinguished from ex post.

3). What determines ex ante demand? Demographics?


Sticky versus Adjusting PricesEx Ante Demand

D D’

Hou

seho

lds e

x po

st

V* (structural vacancy)

S(1-V*) Reduction in Vacancy

Increase in Price

Price


The Baby Boom makes its way through the age distribution[see Eppli-Childs]

Di i l

75+

str bution of households by age of househo d head, 1960-2000

0.0

5.0

10.0

15.0

20.0

25.0

30.0

Under 25 25-34 35-44 45-54 55-64 65-74

Age of Household Head

Mill

ions

of H

ouse

hold

s

1961 - 1970 1971 - 1980 1981 - 1990 1991 - 2000 2001 - 2010

enter for Real EMIT C state Single: rental propensity = 56%

Married: rental propensity = 22%


Clearly demographic factors do not explain all in the housing market: Ownership calculated as if each cohort behaves the same with respect to ownership and only the age distribution changes over time. (dashed line is ex ante, solid line is ex post). The difference = prices! (see: DiPasquale and Wheaton, JUE, 1994).

(Courtesy of Scott Bentley. Used with permission.)

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The importance of correctly measuring “Price” MIT C state

1). Prices versus Quantity versus Expenditure P: price of the same thing over time

(OHHEO index)Q: Physical quantity and quality of space (how to

measure). E = PQ: how much you spend (NAR average price of house that sells)

2). For new houses (1.2m SFU annually) ∆P/P = 4%, ∆Q/Q = 4%, ∆E/E = 8% (1965-1990).

3). For all houses (6m Sales annually) ∆P/P = 7%, ∆Q/Q = .5%, ∆E/E = 7.5%

∆Q/Q >0: new homes better than old + remodeling

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4). Measuring ∆E/E is easy, how to measure ∆P/P?

MIT C state

5). Hedonic equation (again) with time variables for the period each home sells in [D1=1 if sold in period 1, =0 otherwise]. The coefficients on these variables, βi measure the price level in that period relative to the first period in the sample

α2...] eβ1D1+β2D2+... βTDTP = [X1 α1X2

Estimation technique: convert to linear regression . log(P) = α1log(X1)+ α2log(X2)+... + β1D1+ β2D2+... βTDT


6). Repeat sale price index. Look only at homes that sell more than once over the time period. Dependent variable is price change between sale dates. Independent variable is again a set of dummy [0,1] variables for each period. Suppose an observation has the first sale in period i, the next was n periods earlier. T

log(Pi)-log(Pi-n) = Σ βtIt

t=1

For this observation, It is zero for all years except for those in the i to i-n interval. The coefficients βt are then the inflation rate in prices in that year.


7). What does it cost to own one unit measure of Q? This obviously influences how many measures you want.

8). Sometimes it can cost you nothing to own a home. [example: 100k property, 100% LTV, 8% interest, 6% appreciation, 25% marginal tax rate:

After Tax Loan Interest appreciation net

Year 1 100 6 6 0 Year 2 106 6.36 6.36 0 Year 3 112.36 6.72 6.72 0

9). Assumes that you use the additional borrowing each year to offset the interest you just paid. Also assumes no transaction costs [Fleet’s instant Home Equity program].


10). At the end, you have no equity, but were able to enjoy extra consumption of 6% each year. [this is the choice of someone with a high “rate of time preference”]

11). Alternatively, you could not borrow, have 6% less consumption each period and at the end have housing equity to finance your retirement = saving through housing. [choice of someone with a low “rate of time preference”]. How do you finance retirement with housing equity? Reverse mortgage? Downsize? Rent?

12). The discounted value of these two strategies is identical, so the annual cost (in either case) is :

u = P [ i (1- t) - ∆P/P] t = income tax rate 13). Impact of: P (level) – versus - ∆P/P (price appreciation)

The cost of owning for 1st time MIT Center for Real Estate homebuyers in the lowest marginal tax

bracket. [deducting inflation is key]Cost Components of Home Ownership

1978 1980 1982 1984 1986 1988 1990

House Price (1990 dollars) 79666 $79,983 $75,602 $75,076 $76,069 $77,357 $73,706

Mortgage Rate 9.40% 12.53% 14.78% 12.00% 9.80% 9.01% 9.74%

Marginal Tax Rate 22% 21% 19% 18% 18% 15% 15%

Mortgage Amount $63,733 $63,986 $60,481 $60,061 $60,855 $61,885 $58,965

Upfront Cash Required:

Down mayment (20%) $15,933 $15,997 $15,120 $15,015 $15,214 $15,471 $14,741

Closing Costs + $1358 $1,363 $1,288 $1,279 $1,296 $1,318 $1,256

Total: $17,291 $17,359 $16,409 $16,295 $16,510 $16,790 $15,997

Annual Cash Costs:

Mortgage Payment* $6,375 $8,213 $9,049 $7,414 $6,301 $5,981 $6,076

Plus Other Costs** + $3,214 $3,223 $3,268 $3,298 $3,212 $3,107 $2,988

Before-Tax Cash Costs $9,589 $11,435 $12,318 $10,711 $9,513 $9,088 $9,064

Less Tax Savings - $435 $979 $1,201 $899 $655 $266 $308

After-Tax Cash Costs $9,155 $10,456 $11,117 $9,813 $8,848 $8,822 $8,756

Less Nominal Equity Buildup - $8,393 $8,206 $3,810 $2,430 $2,705 $3,274 $1,815

Subtotal: $761 $2,250 $7,307 $7,383 $6,143 $55,48 $6,940

Plus Opportunity Cost + $1,233 $1,742 $1,674 $1,490 $923 $1,103 $1,083

Total Annual Costs: $1,995 $3,992 $8,981 $8,873 $7,067 $6,651 $8,024

*30-yr, fixed rate mortgage. ** Include insurance, maintenance, taxes, fuel, and utilities. adapted from DiPasquale and Wheaton (1996)


Are there Housing “Bubbles”?• Bubble: Housing demand is rising-because prices are rising-because

housing demand is rising! No reason to buy other than the fact that others are buying.

u ⇒ Demand ⇒ Vacancy ⇑ ⇓

Expectations ⇐ Prices ⇐ Sales time

• Watch out if everything has “positive feedback” and is reinforcing everything else. What stops a bubble?

• Marginal buyers who are very sensitive to the price level and not just price inflation and the reduction in u.

• New supply, new supply, new supply! • Are we in a price bubble now? Demographics and low interest rates

say no.


What we do and don’t know about Housing Supply!

• Do construction costs move with the “cycle” (i.e. does land really get all excess profits)?

• Why are construction costs so variable across the country (when many inputs are tradable)?

• How important is “time” or “delay” in adding to cost? More than just interest expense?

• How is the industry organized differently in fast as opposed to slow growing areas?

• Maintenance and Investment in existing structures.

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Construction Costs: Declining gradually in constant $, MIT C state

and immune to the level of building activityFigure 34:

W ashington, DC Apartm ent Construction Real Cost Index vs New Apartm ent Supply

115.00 18,000

16,000 110.00

14,000

105.00 12,000

100.00 10,000

8,000 95.00

6,000

90.00

4,000

85.00 2,000

80.00 0

Bui

ldin

g Pe

rmits

Issu

ed

Cos

t Ind

ex 1

970

= 10

0

iConstruct on Cost Index Apartm ent Build ing Perm its

319

920

719

919

119

319

519

7 919

119

519

719

919

119

3 519

719

120

38 8 9 9

196 6 7 7 7 7 7 8 8 8 9 9 9 0 09 91 1

Year


Housing construction during the cycle: Starts → Inventory → Completions

[Inventory of Units under construction, 1000s]

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How concentrated is the Home building industry MIT C state

at the MSA level?


What impacts the concentration of the Home Building Industry (T. Somerville)?

• Builders are “bigger” in high volume MSA markets (i.e. each builds more).

• Concentration (e.g. top 10 share) does not change depending on market volume.

• Thus high volume markets do not have more, same size builders, but rather the same number of builders that are bigger.

• Equals = Monopolistic competition. • Larger # regulatory agencies (towns) leads to a

greater number of smaller builders. Why?

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Maintenance, Improvements, and expansions as “Supply”

MIT C state

• It is rational to let buildings eventually deteriorate. With discounting, the net benefits of maintenancedecline over time.

• Major improvements, expansions constitute a huge annual market (30% as large as newdevelopment).

• Improvements are “rational” and are more likely to occur when housing is a “good investment” (i.e.low P and high expected ∆P/P ).

• The Elderly improve less = another way ofconsuming your housing equity!

MIT Center for Real Estate Week 9: Housing...

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