Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Bubbles
Anna ScherbinaUC Davis
December 5, 2011
Columbia University
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Frequent failures of present value relations
P0 6= E0
[ ∞∑t=1
CFt
(1 + r)t
]Positive bubbles are more frequent than negative bubbles:
P0 > E0
[ ∞∑t=1
CFt
(1 + r)t
]If r is unknown, using rf instead will provide the highestbound:
P0 > E0
[ ∞∑t=1
CFt
(1 + rf )t
]
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Bubbles were observed through history
Some examples:
The tulip bubble (1634-1637)The South Sea bubble (1720)The Mississippi bubble (1717-1721)The 1920s real estate bubbleThe 1980s Japanese equity and real estate bubblesThe internet bubble (1995-2000)The recent real estate bubble
Bubbles can appear in:
asset classesmarketsindustriesindividual stocks
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
The puzzling Price/Dividend ratio
Why does it increase over time? Is there a growing bubblecomponent in price?
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Agenda
1 Classic bubbles2 Intrinsic bubbles3 Behavioral models
Differences of opinion + short sale constraintsFeedback tradingBiased self-attributionRepresentativeness heuristicLimited arbitrage
4 Incentives & preferences5 Real consequences6 Summary7 Real estate
DataIndexRelevanceCurrent work—extending the index back to 1890
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Expected rate of return
The market index is expected to earn the rate of return rCurrent price:
P0 =Div1
r − g
Next-period price:
P1 =Div1(1 + g)
r − g
One-period return:
P1 + Div1
P0=
Div1(1+g)r−g + Div1
Div1
r−g
=
1+gr−g + 1
1r−g
= 1 + r
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Classic rational bubbles
Agents know the fundamental value but may be willing to paymore
An asset is valued not only for its cash flows but also for itspotential to deliver capital gains
this describes well the prevailing psychology during historicalbubble episodes
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Classic rational bubbles
Suppose that the initial price contains a bubble, B0
Investors are still willing to hold the stock if the bubble growsforever at the required rate of return r
P0 =Div1
r − g+ B0
P1 =Div1(1 + g)
r − g+ B0(1 + r)
...
Pt =Div1(1 + g)t
r − g+ B0(1 + r)t
Check: the one-period return is indeed r :
P1 + Div1
P0=
Div1(1+g)r−g + B0(1 + r) + Div1
Div1r−g + B0
=
(Div1r−g + B0
)(1 + r)
Div1r−g + B0
= 1 + r
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Classic rational bubbles
Time evolution of the bubble: Bt = B0(1 + r)t
As t →∞Bt →∞Pt
Divt→∞
would investors really want to buy a stock with theprice-dividend ratio approaching infinity?
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Solution: “Intrinsic Bubbles: The Case of Stock Prices” byFroot and Obstfeld (AER, 1991)
A bubble is “intrinsic” because its size is assumed to be drivensolely by fundamentals
This parsimonious model is able to:
overcome departures from present value modelsexplain how prices can “overreact” to news aboutfundamentalsexplain bubbles growing and deflating—bubbles do not have togrow to infinity with time
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Intrinsic bubbles
Model the bubble as a function of fundamentals and not oftime
If investors infer too much from current dividend realizations,price may be a function of the current dividend:
B(Divt) = cDivλt
Set λ to match the historical rates of return (r) and dividendgrowth (g)
Bt+1 = Bt(1 + r)
⇒B (Divt+1) = B (Divt) (1 + r)
⇒c [Divt(1 + g)]λ = cDivλt (1 + r)
⇒λ = log1+g (1 + r)
Data:For the U.S.: r = 10.95%, g = 4.76%. Therefore, λ = 2.236
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Estimating the size of the bubble
If there is a bubble, then
Pt =Divt(1 + g)
r − g+ cDivλ
t
Is there a bubble? Run a regression:
Pt
Divt= c0 + cDivλ−1
t + εt
Should expect to find:
c0 = 1+gr−g = 1+4.76%
10.95%−4.76% = 16.91
if r and g are constant and investor expectations equalhistorical averages
c = 0 if there is no bubble or c > 0 if there is a bubble
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Regression results
c0 = 21.91, t-statistic= 14.16
c = 0.78, t-statistic= 9.81
There is a bubble!
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
What is the size of the bubble?
As of December 31, 2010:
B = cDivλ = 0.78× $22.732.236 = $842.27The bubble is 67.84% of the index price P = $1, 241.53!
What if in December 2010 investors expected to earn thehistorical equity risk premium?
Historical ERP = 7.24%. Expected yield on the 3-month t-billis 0.12%These numbers imply that the price should equal:
P2010 =Div2010(1 + g)
ERP + t-bill yield− g
=$22.73(1 + 4.76%)
7.24% + 0.12%− 4.76%= $915.84
This implies a bubble of $325.69, which is 26.23% of the price
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Behavioral models
A subset of investors makes information processing mistakes
Try to match the observed empirical regularities:
the role of short sale constraintspatterns of trading volumethe strong reaction to salient newspost-announcement driftmomentumlong-run reversalsunderperformance of growth stocks, outperformance of valuestocks
Try to address the criticism that arbitrage will eliminate themispricing
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Questions that can be answered
Restrictions of classic bubble models:
no beginningno endno bubbles on finitely-lived assetsa bubble cannot grow faster than the economy
Behavioral models can speak to:
what factors give rise to a bubblewhen a bubble would burst or deflate
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Differences of opinion + short sale constraints
Model 1: Differences of opinion + short sale constraints(Miller (1977))
Investors disagree about the fundamental value of the assets
Short sale constraints prevent pessimistic investors fromselling the asset
Prices reflect the valuation of the optimistic investors
measure investor disagreement with dispersion of analysts’earnings forecasts (Diether, Malloy, Scherbina (2002))measure disagreement with breadth of mutual fund ownership(Chen, Hong and Stein (2001))overvaluation is even larger when a re-sale option is present(Scheinkman and Xiong (2003))
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Differences of opinion + short sale constraints
Model 1: Differences of opinion + short sale constraints
How is a bubble initiated?
a cause for disagreement + short sale constraints
When will the bubble burst?disagreement disappears
prices decline most around earnings announcements(Scherbina (2008))
short sale constraints are relaxed
expiration of lock-up provisions during the internet bubble(Ofek and Richardson (2003))
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Feedback trading
Model 2: Feedback trading
An initial return is in response to news
Feedback traders trade on past price movements and amplifythe initial return
Hong and Stein (1999)DeLong, Shleifer, Summers and Waldmann (1990)
rational speculators will trade with the mispricing to takeadvantage of feedback traders
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Feedback trading
Model 2: Feedback trading
Bubbles coincide with high trading volume
News media amplify feedback trading (Shiller (2002))
media disproportionately covered dot-com stocks during theinternet bubble (Bhattacharya, Galpin, Ray and Yu (2009))
How is a bubble initiated?
justifiable news about the fundamentals
Why would a bubble deflate?
new capital inflow slows down
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Biased self-attribution
Model 3: Biased self-attribution (Daniel, Hirshleifer andSubramanyam (1998))
Self-attribution bias:people pay attention to information that confirms their beliefsand ignore contradictory information as noise
strongest when public signals are ambiguous
The model:
investors form their initial beliefs (receive a private signalabout the asset value)investors then receive a series of public signals about the assetvalueinvestors react to confirming public signals and ignore thecontradicting signalsprices overreact to the private signal
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Biased self-attribution
Model 3: Biased self-attribution
The continuation of price movements is entirely anoverreaction
Can replicate:
momentumlong-run reversal
Prediction: underreaction to selective information events (e.g.,stock issuances, stock repurchases)
Why does a bubble arise?
valid private signal + ambiguous information environment
When will a bubble deflate?
after an accumulation of contradicting public signals
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Representativeness heuristic
Model 4: Representativeness heuristic and conservatismbias (Barberis, Shleifer and Vishny (1998))
Representativeness heuristic:
overreaction to “strong” news with low statistical weight
Conservatism bias:
underreaction to relevant news
The model (loosely based on the two phenomena):
the true earnings model is random walkinvestors mistakenly assume either:
mean-reverting regimetrending regime
“strong” signal: several consecutive realizations of positive(negative) earnings surprises
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Representativeness heuristic
Model 4: Representativeness heuristic and conservatismbias
Can match:
momentumlong-run reversalpost-announcement drift
How is a bubble initiated?
a series of positive shocks (over-extrapolated by investors)“strong” good news
When would a bubble deflate?
an accumulation of signals forces investors to switch fromtrending to mean-reverting model (reversal of sentiment)
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Limited arbitrage
Limits to arbitrage
Arbitrage is risky
mispricing may worsen, and margin calls will force anarbitrageur to abandon the position (Shleifer and Vishny(1997), Xiong (2001), Gromb and Vayanos (2002))fundamentals may change
Trading costs are high (Sadka and Scherbina (2007))
Arbitrageurs may optimally choose to ride the bubble instead(Abreu and Brunnermeier (2003))
hedge funds “rode” the internet bubble (Brunnermeier andNagel (2004))
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
The role of incentives and non-standard preferences
“Keeping up with the Joneses” preferences (DeMarzo, Kanieland Kremer (2008))
Limited liability for borrowers (Allen and Gale (2010))
Herding among money managers:
fund flows and compensation depends on relative performance(Lux (1995))inability to research all stocks (Shiller (2002))labor market incentives (Scharfstein and Stein (1990))investors “force” managers to invest in high-sentiment stocks(Lamont and Frazzini (2008))limited liability for money managers (Allen and Gorton (1993))
Incentive problems:
analystscredit rating agencies
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Real consequences of bubbles
Oversupply of the bubble asset
Effect on real investment, when a bubble asset is used ascollateral (Gan (2007), Chaney, Sraer and Thesmar (2011))
Effect on consumer spending via the wealth channel(Benjamin, Chinloy and Jud (2004), Bostic, Gabriel andPainter (2009), Iacoviello (2011)):
$1 increase in housing wealth ⇒ $0.06-$0.11 increase inannual consumption$1 increase in financial wealth ⇒ $0.02 increase in annualconsumption
When a bubble deflates, transmission to other markets/assets:via the lending channel (Peek and Rosengren (2000))fire sale (Gorton and Metrick (2010))
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Summary
Reasons for a bubble to start
a compelling story (improvements in productivity, land supplyscarcity, etc.)money illusion (real estate)financial liberalizationrise in disagreement + short sale constraints
Reasons for a bubble to deflate
short sale constraint is relaxedsupply of the asset is increasedvaluation uncertainty is resolvedsupply of new capital is exhaustedsentiment is reversedspeculative attackgovernment intervention
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Real estate
Real estate sector is prone to bubbles:
short sale constraintsunsophisticated investors
Shortage of good high-frequency data to estimate real estatereturns
Nicholas and Scherbina (2011) hand-collected real estatetransactions data for Manhattan for 1920-1939 andconstructed a real estate price index
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Why Manhattan?
one of the largest real estate markets in the US
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Why Manhattan?
in 1930 the assessed value of real estate in Manhattan was$9.9 billion while the total real estate value of the country wasaround $266.3 billion
the total value of building plans for Manhattan was “onlyslightly less than 10 percent of the total for 310 United Statescities (Manhattan included) during the same period” (Long(1936))
Manhattan likely performed better than other marketsbecause of high population density
particularly good data exist on transaction prices
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Data and results preview
Data
Real Estate Record and Builders’ Guide7,538 transactions = approximately 30 per month for theperiod 1920-1939housing and location characteristics
Nominal and CPI-adjusted hedonic indices
comovement with repect to the Florida bubblehigh point at the 1929 peak in the stock marketfalls to a low in 1932 and stays there
Key facts
typical house bought in 1920 would have retained only 56% ofits initial value two decades laterthe stock market index outperformed the real estate index by afactor of 4.7 over the period 1920-1939
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Data
Data
RERBG volumes on real estate transactions in New York City
focus on Manhattan though we have also collected summarytransactions for other boroughs
A typical transaction:
Crosby st, 31 (2:473-28) es abt 130 n Grand, 25x100, 7-sty bktnt & str. A$13,500-24,000. 18,500111th st, 140-142 W (7:1820-53) ss, 250 e 7 av. 37.6x100.115-sty tnt FORECLOS A$36,000-60,000. 30,000
Specific data
sale price and sale date and if a foreclosurehousing characteristics: location, square foot, buildingdesignation, construction material, additional features (e.g.,store, basement)tax assessment: value of land, plus value of land and building
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Data
The Real Estate Record and Builders’ Guide
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Data
Summary of the individual transaction data
Price Price/square foot Percent ofYear mean median st. dev. mean median st.dev foreclosures1920 $42,484.46 $25,000.00 $61,900.96 $4.21 $2.67 $5.79 0.00%1921 $40,095.82 $22,000.00 $62,111.79 $3.89 $2.48 $5.27 0.00%1922 $43,318.62 $26,250.00 $60,453.64 $3.99 $2.65 $4.84 0.00%1923 $47,429.89 $28,000.00 $64,410.97 $4.07 $2.69 $5.05 0.00%1924 $44,373.98 $30,000.00 $46,602.85 $4.21 $3.08 $5.02 0.00%1925 $60,610.19 $33,850.00 $81,815.41 $4.96 $3.23 $5.20 0.00%1926 $62,732.08 $35,000.00 $83,834.75 $6.22 $3.79 $7.69 0.00%1927 $61,495.56 $35,250.00 $76,393.15 $5.65 $3.64 $6.62 0.00%1928 $65,875.23 $35,500.00 $90,912.66 $5.64 $3.34 $7.70 0.00%1929 $75,733.53 $40,000.00 $100,976.10 $6.91 $3.82 $8.38 0.00%1930 $56,437.11 $25,000.00 $86,175.37 $4.18 $2.07 $6.77 49.24%1931 $51,335.46 $20,000.00 $81,777.72 $3.12 $1.65 $4.23 60.61%1932 $45,736.64 $20,000.00 $75,643.27 $2.89 $1.61 $4.69 74.19%1933 $59,694.74 $22,000.00 $97,203.78 $2.90 $1.72 $4.65 98.09%1934 $57,102.75 $21,000.00 $95,183.79 $3.10 $1.70 $5.78 100.00%1935 $42,941.29 $20,000.00 $73,950.08 $2.39 $1.69 $2.77 55.38%1936 $37,400.37 $17,500.00 $66,238.79 $2.81 $1.75 $4.77 0.00%1937 $29,869.22 $17,500.00 $43,247.65 $2.61 $1.66 $4.87 0.00%1938 $31,693.69 $15,500.00 $53,855.95 $2.74 $1.53 $5.02 0.00%1939 $30,307.51 $15,000.00 $42,162.28 $2.29 $1.50 $4.22 0.00%All $49,022.87 $25,000.00 $75,317.03 $3.84 $2.22 $5.66 24.94%
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Data
Summary of the individual transaction data
Building designationTenement 52.71%Dwelling 27.69%Loft 10.43%Other 9.17%
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Data
Tenement
Any house or building or portion thereof, which is either rented,leased, let or hired out to be occupied, or is occupied, in whole orin part as the home of residence of three families or more livingindependently of each other and doing their cooking upon thepremises, and includes apartment houses, flat houses and all otherhouses so occupied (Lyle (1920), page 239)
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Data
Lower East Side tenements
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Data
Tenements at Forsythe and E. Houston Streets
a 6-storey tenement on this street sold for $47,000 in December,
1936
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Data
Dwelling at 336 Convent Avenue
sold for $21,500 in January, 1920
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Data
Dwelling at 14 Henderson Place
sold for $24,000 in January, 1926
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Data
Loft
Narrow and tall with long dark interiors, usually built upon one ortwo 25-foot lots previously occupied by brownstones, the buildingswere appropriate for factories or cheap business ventures (Page(2005), page 178)
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Data
Loft at 135 Hudson Street
sold for $110,000 in March, 1923
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Data
Summary of the individual transaction data
Construction materialBrick 72.14%Stone 26.65%Other 1.21%
Additional featuresStore on 1st floor 33.17%Basement 4.58%
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Data
Store at 264 Bowery Street
the loft containing this store sold for $16,000 in January, 1921
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Data
Hester Street Stores (1901)
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Data
Summary of the individual transaction data
Number of storeys1 2.38%2 3.82%3 18.73%4 23.09%5 37.00%6 10.66%7 1.56%8 and over 2.76%
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Data
Summary of the individual transaction data
NeighborhoodsCentral Harlem - Morningside Heights 13.00%Chelsea - Clinton 12.11%East Harlem 10.43%Gramercy Park - Murray Hill 9.71%Greenwich Village - Soho 7.97%Lower Manhattan 4.66%Union Square - Lower East Side 14.69%Upper East Side 11.04%Upper West Side 11.01%Washington Heights - Inwood 5.39%
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Data
Neighborhoods
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Data
Monthly summary data
Additional data for Manhattan, Bronx, Brooklyn, Queens andRichmond
number of transactions, total assessed valuemortgages (total $ value and total number)foreclosures (total $ assessed value and total number)new building permits (total $ value and total number)
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Index
Index construction
Three types:
basic average or median valuesrepeat sales (Case-Shiller; Wheaton et.al. (2009))hedonics
We run a hedonic regression of the sale price of house k attime t on the vector of N house characteristics zkn:
pkt = αtDt +N∑
n=1
zknβn + εkt
where p is the log-sale price and D is the time dummy
the index is the growth rate in αt , computed as exp(αt − α0)
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Index
Regression resultslog(sq. footage) 0.61∗∗∗
(42.07)Tenement -0.62∗∗∗
(-15.83)Dwelling -0.16∗∗∗
(-4.19)Loft -0.14∗∗∗
(-3.28)Brick 0.55∗∗∗
(8.34)Stone 0.63∗∗∗
(9.53)Store on 1st floor 0.20∗∗∗
(7.75)Basement -0.20∗∗∗
(-3.82)1 storey 0.57∗∗∗
(8.54)2 storeys 0.07
(1.14)3 storeys -0.36∗∗∗
(-7.48)4 storeys -0.20∗∗∗
(-4.43)5 storeys -0.15∗∗∗
(-3.32)6 storeys 0.00
(0.03)7 storeys -0.01
(-0.16)
Central Harlem - -0.29∗∗∗
Morningside Heights (-6.07)
Chelsea - Clinton 0.37∗∗∗
(7.69)
East Harlem -0.40∗∗∗
(-8.15)
Gramercy Park - 0.44∗∗∗
Murray Hill (8.71)
Greenwich Village - -0.03Soho (-0.62)
Lower Manhattan 0.32∗∗∗
(5.07)
Union Square - -0.23∗∗∗
Lower East Side (-4.65)
Upper East Side 0.35∗∗∗
(7.18)
Upper West Side 0.22∗∗∗
(4.61)
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Index
The index: quarterly
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Index
The index: annual
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Index
Issues and shortcomings
Seller reservation prices bias up index returns in down markets(Goetzmann and Peng (2003))
Absence of properties that are demolished rather than re-soldintroduces an upward bias in index returns
Is it fair to assume constant prices of housing characteristics?
Aggregate transactions differently?
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Index
Adding net rental income
Net Rental Income = Rental Income - Taxes - OperatingCosts - CAPX
Rental Income, Taxes, Operating Costs (as percentage ofvalue) based on a survey by Burton and Burton (1937)
rental income fell starting in 1930 but taxes and operatingcosts declined significantly less
CAPX is set at 2% per year based on Bolton (1922)
Net Rental Income fluctuates between 6% and -1.3%
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Index
Calculations based on Burton and Burton (1937)
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Index
The stock market index vs. the real estate index thatincludes rental income
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Index
New stock issuance and new construction
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Index
Does the index match other data? New construction
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Index
Does the index match other data? New construction
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Index
Does the index match other data? New construction
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Index
Does the index match other data? New construction
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Index
Does the index match other data? Foreclosures
(a) number, Manhattan (b) $ value, Manhattan
(c) number, Bronx
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Index
Does the index match other data? New mortgages
(d) number, Manhattan (e) $ value, Manhattan
(f) number, Bronx (g) $ value, Bronx
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Index
Does the index match other data? New building permits
(h) Bronx (i) Brooklyn
(j) Richmond (k) Queens
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Index
Heavy tax burden after the crash?
Median ratio of the assessed value to sale price
“Most of the burden of local taxes fell upon real estate...”(Hoyt (1933), p. 269)
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Relevance
Feedback to the economy
From the 1941 NBER report:
“[Real estate]... is one of the greatest outlets for long terminvestment by banks, insurance companies, and privateinvestors, and economic stability generally is influenced in alarge degree by what happens in real estate. The Committeewas of the opinion also that real estate financing had beencommonly understressed in the discussions of banking andcredit phases of stabilization problems...”In 1920, total mortgage debt outstanding was $9.35 billion(10.2% of household wealth)In 1929, it increased to $29.44 billion (27.2% of householdwealth)In 1930, total urban real estate had value of $266.30 billion ofwhich $122.60 billion represented urban residential properties51% of urban residential properties were mortgaged
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Relevance
Mortgage loan holding agencies
Loan Holding Agency Distribution (based on aggregate loans, indollars), as of 1934
Source: NBER publication number 38, 1941, Table D48
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Relevance
Conclusion
There seems to have been a real estate bubble in the 1920s
It crashed at the same time as the stock market
The recovery was slower than for the stock market
an oversupply of real estate?
Households and banks suffered significant losses, which likelycontributed to the prolonged recession
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Current work—extending the index back to 1890
The 1890-1939 period. Many crises: expected returns =actual returns
Panic of 1893
1893Q1 - 1894Q4. Caused by deflation (mismatch betweenproduction and consumption). High unemployment, taxincreases. Over-supply in housing construction, rents reducedto foster demand. “Real estate is a liability instead of anasset” (Hoyt (1933))
Panic of 1907
1907Q2 - 1908Q2. Banking panic, demand for liquidity. Likelyhad a larger effect on the stock market than real estate.
WW1
Not a NBER recession. US delays involvement until 1917, butmobilization large after that. War needs crowd otherinvestments, housing starts below predicted trends (White2009).
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Current work—extending the index back to 1890
The 1890-1939 period. Many crises: expected returns =actual returns
Depression of 1920-21
1920Q1 - 1921Q3. Heavy deflation caused by post-warreadjustment; high interest rates, low government spending,high unemployment. Quick rebound into “roaring twenties.”
Florida Bubble collapse, September 1926
Bubble caused by speculators flipping properties. Hurricanedamage caused the bubble to burst(?) Prelude to the GreatDepression (White (2009)).
The Great Crash
1929Q3 - 1933Q1
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Current work—extending the index back to 1890
Without dividends and net rental income
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Current work—extending the index back to 1890
Without dividends and net rental income
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Current work—extending the index back to 1890
With dividends and net rental income
Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate
Current work—extending the index back to 1890
With dividends and net rental income
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