bubbles - industrial engineering and operations …...the mississippi bubble (1717-1721) the 1920s...

Classic bubbles Intrinsic bubbles Behavioral models Incentives & preferences Real consequences Summary Real estate

Bubbles

Anna ScherbinaUC Davis

December 5, 2011

Columbia University


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

]


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


The puzzling Price/Dividend ratio

Why does it increase over time? Is there a growing bubblecomponent in price?


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


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 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



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



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?


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


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


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


Regression results

c0 = 21.91, t-statistic= 14.16

c = 0.78, t-statistic= 9.81

There is a bubble!


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


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


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


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))


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))


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


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))


justifiable news about the fundamentals

Why would a bubble deflate?

new capital inflow slows down


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


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


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


Representativeness heuristic

Model 4: Representativeness heuristic and conservatismbias

Can match:

momentumlong-run reversalpost-announcement drift


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)


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))


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


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))


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


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


Why Manhattan?

one of the largest real estate markets in the US


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


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


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


Data

The Real Estate Record and Builders’ Guide


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%


Data


Building designationTenement 52.71%Dwelling 27.69%Loft 10.43%Other 9.17%


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)


Data

Lower East Side tenements


Data

Tenements at Forsythe and E. Houston Streets

a 6-storey tenement on this street sold for $47,000 in December,

1936


Data

Dwelling at 336 Convent Avenue

sold for $21,500 in January, 1920


Data

Dwelling at 14 Henderson Place

sold for $24,000 in January, 1926


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)


Data

Loft at 135 Hudson Street

sold for $110,000 in March, 1923


Data


Construction materialBrick 72.14%Stone 26.65%Other 1.21%

Additional featuresStore on 1st floor 33.17%Basement 4.58%


Data

Store at 264 Bowery Street

the loft containing this store sold for $16,000 in January, 1921


Data

Hester Street Stores (1901)


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%


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%


Data

Neighborhoods


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)


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)


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)


Index

The index: quarterly


Index

The index: annual


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?


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%


Index

Calculations based on Burton and Burton (1937)


Index

The stock market index vs. the real estate index thatincludes rental income


Index

New stock issuance and new construction


Index

Does the index match other data? New construction


Index

Does the index match other data? Foreclosures

(a) number, Manhattan (b) $ value, Manhattan

(c) number, Bronx


Index

Does the index match other data? New mortgages

(d) number, Manhattan (e) $ value, Manhattan

(f) number, Bronx (g) $ value, Bronx


Index

Does the index match other data? New building permits

(h) Bronx (i) Brooklyn

(j) Richmond (k) Queens


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)


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


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


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


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).



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



Without dividends and net rental income



With dividends and net rental income

bubbles - industrial engineering and operations …...the mississippi bubble (1717-1721) the 1920s...

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