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Leverage and House-price Dynamics Under the Recourse-Loan System in Japanese Prefecures
Abstract
We use Japanese prefectural-level data to analyze the relationship between borrowing patterns and house price dynamics under the recourse-loan system. Our principal finding is that in prefectures where highly leveraged homeowners are common– i.e. have high, extended loan-to-value ratios- house prices respond less to prefecture-specific shocks, such as changes in per-capita income. This finding based on the recourse-loan system is quite different from the finding under the non-recourse loan system, because under the recourse-loan system, the lock-in effect stemming from severe equity constraints is much more severe.
The first part of this chapter draws on 12 waves of Japanese household longitudinal data (Keio Household Panel Survey, KHPS) and estimates a conditional fixed effects logit model to investigate the effects of housing equity constraints and income shocks on own-to-own residential moves in Japan by comparing the effects between 2004 to 2008 and 2009 to 2014. By looking at contemporaneous extended Loan-to-Value (ELTV) and extended Debt-to-Income (EDTI) ratios under the recourse-loan system, we examine whether housing equity constraints and negative income shocks have any impact on own-to-own residential moves and whether there is any difference between the two periods. Taking account of the specific nature of the recourse-loan system in Japan, we further investigate whether these effects differ between positive and negative equity households. The estimation results show that housing equity constraints and negative income shocks significantly deter own-to-own residential moves for positive equity households even in recent financial-easing periods. In the latter part of this chapter, we use Japanese prefectural-level data to analyze the relationship between borrowing patterns and house price dynamics under the recourse-loan system. Our principal finding is that, in prefectures where homeowners are highly leveraged (i.e., have high and extended loan-to-value ratios), house prices respond less sensitively than they do in prefectures where lower leveraged homeowners are common. This finding based on the recourse-loan system is quite different from the finding under the non-recourse-loan system, because under the recourse-loan system, the lock-in effect stemming from severe equity constraints is much more severe.
In this chapter we study the asymptotic distributions for ordinary least squares (OLS), fully modified OLS (FMOLS), and dynamic OLS (DOLS) estimators in cointegrated regression models in panel data. We show that the OLS: FMOLS, and DOLS estimators are all asymptotically normally distributed. However; the asymptotic distribution of the OLS estimator is shown to have a non-zero mean. Monte Carlo results illustrate the sampling behavior of the proposed estimators and show that (I) the OLS estimator has a non-negligible bias in finite samples, (2) the FMOLS estimator does not improve over the OLS estimator in general, and (3) the DOLS outperforms both the OLS and FMOLS estimators.
This paper draws on six waves of Japanese household longitudinal data (Keio Household Panel Survey, KHPS) and estimates a
conditional fixed effects logit model to investigate the effects of housing equity constraints and income shocks on own-to-own
residential moves in Japan. By looking at contemporaneous extended Loan-to-Value (ELTV) and extended Debt-to-Income (EDTI)
ratios under the recourse loan system, we examine whether housing equity constraints and negative income shocks have any impact
on own-to-own residential moves. Taking account of the specific nature of the recourse loan system in Japan, we further investigate
whether these effects are different between positive and negative equity households. The estimation results show that housing
equity constraints and negative income shocks significantly deter own-to-own residential moves for positive equity households.
KeywordsResidential mobility–Housing equity constraint–Conditional fixed effects logit model–Loan-to-value ratio–Debt-to-income ratio–Negative equity–Japan–Recourse loan
The paper uses quarterly indexes of existing single-family home prices estimated with microdata on properties that sold more than once to estimate excess returns to investment in owner-occupied housing. Housing prices and excess returns are estimated over the period 1970:1 to 1986:3 for Atlanta, Chicago, Dallas, San Francisco. Using time-series cross-section regressions we test for the forecastability of prices and excess returns using a number of independent variables. Price changes in one year tend to continue for more than one year in the same direction. The ratio of construction costs to price, changes in adult population and increases in real per capita income all are positively related to excess returns or price changes over the subsequent year. The results add weight to the argument that the market for single-family homes is inefficient. Copyright American Real Estate and Urban Economics Association.
We examine the effect of US branch banking deregulations on the entry size of new firms using micro-data from the US Census Bureau. We find that the average entry size for startups did not change following the deregulations. However, among firms that survived at least four years, a greater proportion of firms entered either at their maximum size or closer to the maximum size in the first year. The magnitude of these effects were small compared to the much larger changes in entry rates of small firms following the reforms. Our results highlight that this large-scale entry at the extensive margin can obscure the more subtle intensive margin effects of changes in financing constraints.
In the first half of the paper I study spurious regressions in panel data. Asymptotic properties of the least-squares dummy variable (LSDV) estimator and other conventional statistics are examined. The asymptotics of LSDV estimator are different from those of the spurious regression in the pure time-series. This has an important consequence for residual-based cointegration tests in panel data, because the null distribution of residual-based cointegration tests depends on the asymptotics of LSDV estimator.In the second half of the paper I study residual-based tests for cointegration regression in panel data. I study Dickey–Fuller (DF) tests and an augmented Dickey–Fuller (ADF) test to test the null of no cointegration. Asymptotic distributions of the tests are derived and Monte Carlo experiments are conducted to evaluate finite sample properties of the proposed tests.
There is a relatively recent, growing recognition about the importance of the interactive nexus between and among housing markets and the macroeconomy. This paper provides a selective survey of the small but nascent research efforts focused upon interplay between the housing markets and macroeconomy. The discussion examines these lynchpin housing–macroeconomic nexus issues pertaining to housing taxation, housing cycles, and housing market–urban structural form.
To test the hypothesis of a difference stationary time series against a trend stationary alternative, Levin & Lin (1993) and Im, Pesaran & Shin (1997) suggest bias adjusted t-statistics. Such corrections are necessary to account for the nonzero mean of the t-statistic in the case of an OLS detrending method. In this chapter the local power of panel unit root statistics against a sequence of local alternatives is studied. It is shown that the local power of the test statistics is affected by two different terms. The first term represents the asymptotic effect on the bias due to the detrending method and the second term is the usual location parameter of the limiting distribution under the sequence of local alternatives. It is argued that both terms can offset each other so that the test has no power against the sequence of local alternatives. These results suggest to construct test statistics based on alternative detrending methods. We consider a class of t-statistics that do not require a bias correction. The results of a Monte Carlo experiment suggest that avoiding the bias can improve the power of the test substantially.
Asymptotic distributions and critical values are computed for several residual-based tests of the null of no cointegration in panels for the case of multiple regressors, including regressions with individual-specific fixed effects and time trends. The associated cointegrating vectors and the dynamics of the underlying error processes are permitted considerable heterogeneity across individual members of the panel.
This paper studies the relationship between residential property prices and bank lending in Hong Kong. This is an interesting topic for three reasons. First, swings in property prices have been extremely large and frequent in Hong Kong. Second, under the currency board regime, monetary policy cannot be used to guard against asset price swings. Third, despite the collapse in property prices since 1998, the banking sector remains sound. While the contemporaneous correlation between lending and property prices is large, our results suggest that the direction of influence goes from property prices to bank credit rather than conversely.
This paper analyzes a simple model of an urban area with growth and uncertainty. Household income, rents, and prices for land follow stochastic processes. Even though investors are risk neutral, uncertainty affects both land rents and land prices in equilibrium because the conversion of land from agricultural to urban use is irreversible. Growth, on the other hand, affects urban and agricultural land prices but not the level of rents. We show that uncertainty (i) delays the conversion of land from agricultural to urban use, (ii) imparts an option value to agricultural land, (iii) causes land at the boundary to sell for more than its opportunity cost in other uses, and (iv) reduces equilibrium city size.
This article argues that, especially in the absence of sufficient direct data on credit constraints, it is reasonable to add a household debt variable in an empirical model studying housing price dynamics. This is because household borrowing is likely to reveal information regarding the credit constraints faced by households. Moreover, debt may also give information on expected income growth and interest rate movements. The aim of this study is to examine empirically if household borrowing data, indeed, is of importance in a dynamic housing price model. In line with the prior expectations, it is found that housing appreciation in the Helsinki Metropolitan area is Granger caused by the household debt-to-GDP ratio both in the short and in the long run. Causality from the housing market to credit, in turn, seems to run only through a cointegrating long-run relation. While the estimated long-run relation between housing prices, income and debt-to-GDP ratio appears to have remained stable through the sample period (1975Q1-2006Q2), the short-run dynamics changed somewhat due to the financial liberalization in the late 1980s. The stability of the long-run relation implies that the loan data are able to cater, at least to a notable extent, for the effect of the changes in Finnish households’ liquidity constraints on housing demand. In line with previous literature, it is also found that housing price adjustment is sluggish and includes notable backward-looking features.
This paper presents a simple model of trade in the housing market. The crucial feature is that a minimum down payment is required
for the purchase of a new home. The model has direct implications for the volatility of house prices, as well as for the correlation
between prices and trading volume. The model can also be extended to address the correlation between prices and time-to-sale,
as well as certain aspects of the cyclical behavior of housing starts.
In this paper, we study the asymptotic distributions for least-squares (OLS), fully modified (FM), and dynamic OLS\ (DOLS) estimators in cointegrated regression models in panel data. We show that the OLS, FM, and DOLS estimators are all asymptotically normally distributed. However, the asymptotic distribution of the OLS estimator is shown to have a non-zero mean. Monte Carlo results examine the sampling behavior of the proposed estimators and show that (1) the OLS estimator has a non-negligible bias in finite samples, (2) the FM estimator does not improve over the OLS estimator in general, and (3) the DOLS out-performs both the OLS and FM estimators.
This research analyzes the dynamic properties of the difference equation that arises when markets exhibit serial correlation and mean reversion. We identify the correlation and reversion parameters for which prices will overshoot equilibrium ("cycles") and/or diverge permanently from equilibrium. We then estimate the serial correlation and mean reversion coefficients from a large panel data set of 62 metro areas from 1979 to 1995 conditional on a set of economic variables that proxy for information costs, supply costs and expectations. Serial correlation is higher in metro areas with higher real incomes, population growth and real construction costs. Mean reversion is greater in large metro areas and faster growing cities with lower construction costs. The average fitted values for mean reversion and serial correlation lie in the convergent oscillatory region, but specific observations fall in both the damped and oscillatory regions and in both the convergent and divergent regions. Thus, the dynamic properties of housing markets are specific to the given time and location being considered. Copyright 2004 by the American Real Estate and Urban Economics Association
Asymptotic distributions and critical values are computed for several residual-based tests of the null of no cointegration in panels for the case of multiple regressors, including regressions with individual-specific fixed effects and time trends. The associated cointegrating vectors and the dynamics of the underlying error processes are permitted considerable heterogeneity across individual members of the panel. Copyright 1999 by Blackwell Publishing Ltd
How does one tell when rapid growth in house prices is caused by fundamental factors of supply and demand and when it is an unsustainable bubble? In this paper, we explain how to assess the state of house prices--both whether there is a bubble and what underlying factors support housing demand--in a way that is grounded in economic theory. In doing so, we correct four common fallacies about the costliness of the housing market. For a number of reasons, conventional metrics for assessing pricing in the housing market such as price-to-rent ratios or price-to-income ratios generally fail to reflect accurately the state of housing costs. To the eyes of analysts employing such measures, housing markets can appear "exuberant" even when houses are in fact reasonably priced. We construct a measure for evaluating the cost of home owning that is standard for economists--the imputed annual rental cost of owning a home, a variant of the user cost of housing--and apply it to 25 years of history across a wide variety of housing markets. This calculation enables us to estimate the time pattern of housing costs within a market. As of the end of 2004, our analysis reveals little evidence of a housing bubble.
This paper proposes unit root tests for dynamic heterogeneous panels based on the mean of individual unit root statistics. In particular it proposes a standardized t-bar test statistic based on the (augmented) Dickey–Fuller statistics averaged across the groups. Under a general setting this statistic is shown to converge in probability to a standard normal variate sequentially with T (the time series dimension) →∞, followed by N (the cross sectional dimension) →∞. A diagonal convergence result with T and N→∞ while N/T→k,k being a finite non-negative constant, is also conjectured. In the special case where errors in individual Dickey–Fuller (DF) regressions are serially uncorrelated a modified version of the standardized t-bar statistic is shown to be distributed as standard normal as N→∞ for a fixed T, so long as T>5 in the case of DF regressions with intercepts and T>6 in the case of DF regressions with intercepts and linear time trends. An exact fixed N and T test is also developed using the simple average of the DF statistics. Monte Carlo results show that if a large enough lag order is selected for the underlying ADF regressions, then the small sample performances of the t-bar test is reasonably satisfactory and generally better than the test proposed by Levin and Lin (Unpublished manuscript, University of California, San Diego, 1993).
This paper examines whether electoral motives and government ideology influence short-term economic performance. I employ data on annual GDP growth in 21 OECD countries over the 1951-2006 period and provide a battery of empirical tests. In countries with two-party systems GDP growth is boosted before elections and, under leftwing governments, in the first two years of a legislative period. These findings indicate that political cycles are more prevalent in two-party systems because voters can clearly punish or reward political parties for governmental performance. My findings imply that we need more elaborate theories of how government ideology and electoral motives influence short-term economic performance.
The asymptotic distributions of cointegration tests are approximated using the Gamma distribution. The tests considered are for the I(1), the conditional I(1), as well as the I(2) model. Formulae for the parameters of the Gamma distributions are derived from response surfaces. The resulting approximation is flexible, easy to implement and more accurate than the standard tables previously published.
The panel data unit root test suggested by Levin and Lin (LL) has been widely used in several applications, notably in papers on tests of the purchasing power parity hypothesis. This test is based on a very restrictive hypothesis which is rarely ever of interest in practice. The Im-Pesaran-Shin (IPS) test relaxes the restrictive assumption of the LL test. This paper argues that although the IPS test has been offered as a generalization of the LL test, it is best viewed as a test for summarizing the evidence from a number of independent tests of the sample hypothesis. This problem has a long statistical history going back to R. A. Fisher. This paper suggests the Fisher test as a panel data unit root test, compares it with the LL and IPS tests, and the Bonferroni bounds test which is valid for correlated tests. Overall, the evidence points to the Fisher test with bootstrap-based critical values as the preferred choice. We also suggest the use of the Fisher test for testing stationarity as the null and also in testing for cointegration in panel data. Copyright 1999 by Blackwell Publishing Ltd
This paper studies the relationship between city-level house-price dynamics and homeowner borrowing patterns. We ask whether house prices respond more sensitively to economic shocks--in particular, to changes in per-capita income--in cities where a large fraction of homeowners are highly leveraged. For the purposes of this study, the basic concept of individual homeowner leverage is the ratio of the outstanding mortgage balance to the current value of the house, commonly known as the loan-to-value (LTV) ratio.
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