Natural Expectations and Home Equity Extraction

Transcription

Natural Expectations and Home Equity Extraction
Roberto Pancrazi1
1
2
Mario Pietrunti
2
University of Warwick
Toulouse School of Economics, Banca d’Italia
4 December 2013
AMSE
Pancrazi, Pietrunti ( University of Warwick, Toulouse School of Economics,Natural
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Introduction
Motivation
Spark of the recent global crisis: the 2007 mortgage crisis
Boom of collateralized credit market related to housing wealth
Economy excessively exposed to an unpredicted fall of house prices
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Introduction
Why Economy Excessively Exposed?
1
Financial instruments made housing a liquid asset
2
House prices are hard to predict
These two ingredients likely to create distortions
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Introduction
Intuition
Simply from the Permanent Income Hyphotesis...
Households have biased expectations about future housing prices
Incentive to borrow today to consume perceived higher future housing wealth
Transform future housing wealth in present consumption with Home Equity
Loans
Banks’ optimism allows households’ borrowing.
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Introduction
Home Equity Extraction
Housing wealth becoming a liquid asset
Home Equity Line of Credit (HELOC): loan in which lender lend a maximum
amount within an agreed period (term), where the collateral is the borrower’s
equity in her house.
Ms. Kennedy says that with the $70,000 from the cash-out refinancing
she got this year, she is investing in the stock market and considering
buying another home for investment purposes. Her current home surged
in value by $134,000 over the past 16 years. Now, her goal is to turn her
cash-out windfall into $250,000 over the next 10 to 15 years. ”I just
didn’t want to let $70,000 sit in my home”
Wall Street Journal, July 26, 2001
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Introduction
Home Equity Extraction
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Introduction
What we do: Empirically
Parsimoniously model biased expectation for house price
1
Natural agents fail to estimate the medium- long-run properties of house
prices
2
Document that financial expert had biased expectations
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Introduction
What we do: Theoretically
Modelling Natural Expectations and Home Equity Extraction
Households and Banks meet in the credit market
Households pledge their house as collateral to obtain credit.
Allow for both agents being natural
Assess the effect of biased expectations during 2000-2010
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Introduction
Findings
Quantifying the Role of Natural Expections
Debt to Income Ratio: 16% higher for natural households during boom
Interest Rate: lower for natural agents
Vulnerability: higher for natural agents when house prices decline
Biased expectation contributed to Economy Excessively Exposed
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House Price and HEE
Outline
House Prices and Home Equity Exctraction
Modelling Home Equity Extraction
Natural Expectations for House Price
Quantifying the Role of Optimism in the Credit Market
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House Price and HEE
Home Equity Loans
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
1995
2000
2005
2010
Figure: Revolving Home Equity Loans in the U.S.: Trillion of dollars
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House Price and HEE
Home Equity Loans and House Prices
6
200
5
180
4
160
3
140
2
120
1
1995
2000
2005
100
Figure: Home Equity Loans-Disposable Income ratio and U.S. House Price index
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House Price and HEE
Home Equity Extraction and Education
Education Category
1
2
3
4
5
6
Average
1998
19.3
23.7
15.8
14.8
13.5
14.2
14.1
HELOC Exposure
2001
2004
2007
13.4
31.5
5.7
7.5
20.5
11.7
15.5
16.0
9.8
14.9
12.8
15.9
13.1
16.4
11.8
19.8
16.3
14.7
16.2
16.9
13.0
HELOC Participation
1998
2001
2004
2007
0.3
2.2
3.5
3.2
2.3
2.4
2.4
3.6
3.8
3.6
6.4
8.0
4.5
4.9
7.7
9.4
6.0
7.0
12.0
10.6
7.8
6.4
12.7
13.6
4.8
5.0
8.9
9.5
Table: Heloc for Education Categories: Survey of Consumer Finance
Duca and Kumar (2014): Financial illiteracy affected Home Equity Extraction
Agurwar and Mazumder (2013): Cognitive ability affects House price forecast
precision
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Model
Outline
1
2
3
4
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Model
Elements of the model
Infinitely lived economy
Two agents: Household and Bank
Two goods: consumption and housing
Household owns house and pledge it as collateral
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Model
Household Problem
max{ct ,dt ,deft }Tt=0 EH
0
T
X
β t u(ct , h),
t=0
deft =0: ct + (1 + rt−1 )dt−1 = y + dt
deft =1:
ct + γpt h = y
Expectation Bias
h
pt+1 = pt 1 + rt+1
,
h
rt+1
= Θp (L)rth + σεt+1 , ,
Default Probability
1
1 + rt
Pr {deft+1 = 1} = Pr εt+1 ≤
dt − 1 − Θp (L)rth
σ pt (1 + γ)h
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Model
Bank Problem

(rt−1 − it−1 )dt−1 + δEBt πt+1 (rt , dt , pt+1 )





if the household does not default





(and did not default in the past)








κpt h − (1 + it−1 )dt−1


max πt (rt−1 , dt−1 , pt ) = if the household defaults
dt



(but did not in the past)









0





if the household defaulted



in the past.
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Outline
1
2
3
4
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Question for you
What was the real appreciation of the average U.S. house price (index) from 1955
to 2000?
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Question for you
to 2000?
Answer: 0%!
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Question for you
to 2000?
Answer: 0%!
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U.S. House Prices
5.4
5.2
5
4.8
4.6
1955
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
2010
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
2010
0.1
0
−0.1
−0.2
1955
Figure: Real U.S. House Price index (upper panel) and its growth rate (lower panel)
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Natural and Rational Agents
(1 − Φp (L)) rt = µ + εt
Natural agents estimate and forecast using AR(1)
[Fuster et al. (2012), Hommes and Sorger (1998), Hommes and Zha (2013)]
Rational (Sophisticated) agents estimate and forecast using BIC/AIC
BIC: p=5 // AIC: p=16
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Natural and Rational Estimates
p
φ1
φ2
φ3
φ4
φ5
φ6
φ7
φ8
φ9
φ10
φ11
φ12
φ13
φ14
φ15
φ16
Natural
1
0.958∗∗∗
[0.02]
Whole Sample: 1953:1-2010:4
Sophisticated BIC
Sophisticated AIC
5
16
1.330∗∗∗
1.348∗∗∗
[0.06]
[0.07]
−0.221∗∗
−0.241∗∗∗
[0.10]
[0.11]
0.09
0.122
[0.10]
[0.12]
∗∗∗
−0.614
−0.841∗∗∗
[0.10]
[0.12]
0.355∗∗∗
0.656∗∗∗
[0.06]
[0.12]
0.012
[0.13]
−0.060
[0.13]
−0.457∗∗∗
[0.13]
0.346∗∗∗
[0.13]
0.055
[0.13]
0.121
[0.14]
−0.631∗∗∗
[0.13]
0.285∗∗
[0.12]
0.050
[0.13]
0.136
[0.13]
−0.119
[0.08]
Natural
1
0.914∗∗∗
[0.00]
φ17
σ
0.014
0.012
0.011
0.014
R2
0.91
0.94
0.95
0.83
Pancrazi,
Pietrunti (23.7
University of Warwick,
Economics,Natural
Banca d’Italia)
HEE 11.6
LRP
18.3Toulouse School of10.4
Subsample: 1953:1-1996:4
Sophisticated BIC
Sophisticated AIC
13
17
1.052∗∗∗
1.118∗∗∗
[0.08]
[0.08]
−0.024
−0.136
[0.11]
[0.13]
0.1131
0.194
[0.11]
[0.12]
∗∗∗
−0.695
−0.805∗∗∗
[0.11]
[0.12]
−0.540∗∗∗
0.652∗∗∗
[0.14]
[0.12]
0.077
0.004
[0.13]
[0.14]
−0.073
−0.006
[0.13]
[0.14]
−0.459∗∗∗
−0.562∗∗∗
[0.13]
[0.14]
0.425∗∗∗
0.485∗∗∗
[0.12]
[0.14]
0.049
0.013
[0.11]
[0.14]
0.039
0.148
[0.11]
[0.14]
−0.467∗∗∗
−0.653∗∗∗
[0.11]
[014]
0.218∗∗∗
0.403∗∗
[0.08]
[0.14]
−0.118
[0.12]
0.211∗
[0.12]
−0.291∗∗
[0.08]
0.105
[0.08]
0.093
0.090
0.89
0.90
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Natural and Rational Impulse Response
(1 − Φp (L)) rt = µ + εt
2
Naive: p=1
Soph.BIC: p= 5
Soph.AIC: p=16
1.5
1
0.5
0
−0.5
−1
0
10
20
30
40
50
60
70
80
90
100
90
100
25
20
15
10
Naive: p=1
Soph.BIC: p= 5
Soph.AIC: p=16
5
0
0
10
20
30
40
50
60
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70
80
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Natural Financial Experts
We gather and analyze a unique data set.
Source: Moody’s Analytics
Out-of-sample forecasts by using a rich demand-supply model (income trends,
demographics cyclical factors)
Quarterly forecasts from 1995-2011
30 years horizon (quarterly)
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Financial Experts Forecast
180
170
160
1997
2000
2002
2004
2006
Realized
150
140
130
120
110
100
90
80
1990
1995
2000
2005
2010
2015
2020
2025
2030
2035
Figure: Financial Experts Real House Price Out-of-Sample Forecasts
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Natural Financial Experts
5.4
5.3
AR1-Forecast
AR13-Forecast
Actual Forecast
Realized Prices
5.2
5.1
5
4.9
4.8
4.7
4.6
2000
2005
2010
2015
2020
Figure: Natural Forecast, Rational Forecast, and Financial Expert’s Forecast
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Outline
1
2
3
4
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Empirical Strategy
Discretize house price growth rate (3 nodes)
Solve the model by backward induction
Natural Agents: AR(1)
Rational Agents (DGP): AR(2)
ρ1
ρ2
σ
Long-Run Persistence (LRP)
Standard Deviation
First-order Autocorrelation
Natural - AR(1)
0.72
0.033
3.59
0.034
0.724
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Rational - AR(2)
0.97
-0.58
0.027
1.63
0.027
0.728
Annual Data
1.63
0.027
0.728
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Calibration
Parameter
β=δ
h
η
y
γ
κ
Value
0.98
1.5
2
1
0.05
0.2
Description
Discount rate for household and banks
Housing stock
CRRA coefficient
Income per year
Rental rate as a fraction of house value
Collateral value for the bank as a fraction of house value
housing price
1.4
1.2
1
1
2
3
4
5
6
7
8
9
Figure: Simulated house price Dynamics (2001-2009)
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During House Price Boom
(1) Both Rational, (2) Both Natural,
1
Natural household extracts more than 2 times than rational
2
At a lower interest rate
3
Natural household consumes more
Natural household 4 times more vulnerable
4
Scenario
Debt to income (%)
Cons to Income (%)
Loan to Value (%)
Interest Rate (%)
Debt Service Ratio (%)
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Boom
(1)
(2)
8.8
21.9
4.92 4.96
4.64 12.49
3.17 2.95
3.83 16.93
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During House Price Bust
1
Abrupt deleveraging for natural household
2
Similar level of vulnerability
3
Rational household keeps constant DRS
Scenario
Debt to income (%)
Cons to Income (%)
Loan to Value (%)
Interest Rate (%)
Debt Service Ratio (%)
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Bust
(1)
(2)
8.1
5.9
-10.8 -11.4
4.16
3.07
2.11
2.11
18.87 17.33
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Conclusions
Conclusions
Housing: asset with large value and hard to forecast
Financial market for housing: exposure to biased expectations
Using a quantitative model, we find large effects due to biased expectations
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Natural Expectations and Home Equity Extraction

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