Abstract

We propose a new method to estimate quality adjusted commercial property price indexes using real estate investment trust (REIT) data. Our method is based on the present value approach, but the way the denominator (i.e., the discount rate) and the numerator (i.e., cash flows from properties) are estimated differs from the traditional method. We run a hedonic regression to estimate the quality adjusted discount rate based on the share prices of REITs, which can be regarded as the stock market’s valuation of the set of properties owned by the REITs. As for the numerator, we use rental prices associated only with new rental contracts rather than those associated with all existing contracts. Using a dataset with prices and cash flows for about 400 commercial properties included in Japanese REITs for the period 2001 to 2013, we find that our price index signals turning points much earlier than an appraisal-based price index; specifically, our index peaks in the second quarter of 2007, while the appraisal-based price index exhibits a turnaround only in the third quarter of 2008. Our results suggest that the share prices of REITs provide useful information in constructing commercial property price indexes.

Introduction

Looking back at the history of economic crises, there are a considerable number of cases where a crisis was triggered by the collapse of a real estate price bubble. For example, it is widely accepted that the collapse of Japan’s land and stock price bubble in the early 1990s has played an important role in the subsequent economic stagnation, and in particular the banking crisis that started in the latter half of the 1990s. Similarly, the Nordic banking crisis in the early 1990s also occurred in tandem with a property bubble collapse, while the global financial crisis that began in the United States in 2008 and the European debt crisis were also triggered by the collapse of bubbles in the property and financial markets.

2015.02

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2013.12

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Abstract

Since real estate is heterogeneous and infrequently traded, the repeat sales model has become a popular method to estimate a real estate price index. However, the model fails to adjust for depreciation, as age and time between sales have an exact linear relationship. This paper proposes a new method to estimate an age-adjusted repeat sales index by decomposing property value into land and structure components. As depreciation is more relevant to the structure than land, the property’s depreciation rate should depend on the relative size of land and structure. The larger the land component, the lower is the depreciation rate of the property. Based on housing transactions data from Hong Kong and Tokyo, we find that Hong Kong has a higher depreciation rate (assuming a fixed structure-to-property value ratio), while the resulting age adjustment is larger in Tokyo because its structure component has grown larger from the first to second sales.

Introduction

A price index aims to capture the price change of products free from any variations in quantity or quality. When it comes to real estate, the core problem is that it is heterogeneous and infrequently traded. Mean or median price indices are simple to compute, but properties sold in one period may differ from those in another period. To overcome this problem, two regression-based approaches are used to construct a constant-quality real estate price index (Shimizu et al. (2010)).

2013.11

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Abstract

Dramatic increases and decreases in housing prices have had an enormous impact on the economies of various countries. If this kind of fluctuation in housing prices is linked to fluctuations in the consumer price index (CPI) and GDP, it may be reflected in fiscal and monetary policies. However, during the 1980s housing bubble in Japan and the later U.S. housing bubble, fluctuations in asset prices were not sufficiently reflected in price statistics and the like. The estimation of imputed rent for owneroccupied housing is said to be one of the most important factors for this. Using multiple previously proposed methods, this study estimated the imputed rent for owner-occupied housing in Tokyo and clarified the extent to which the estimated imputed rent diverged depending on the estimation method. Examining the results obtained showed that, during the bubble’s peak, there was an 11-fold discrepancy between the Equivalent Rent Approach currently employed in Japan and Equivalent Rent calculated with a hedonic approach using market rent. Meanwhile, with the User Cost Approach, during the bubble period when asset prices rose significantly, the values became negative with some estimation methods. Accordingly, we estimated Diewert’s OOH Index, which was proposed by Diewert and Nakamura (2009). When the Diewert’s OOH Index results estimated here were compared to Equivalent Rent Approach estimation results modified with the hedonic approach using market rent, it revealed that from 1990 to 2009, the Diewert’s OOH Index results were on average 1.7 times greater than the Equivalent Rent Approach results, with a maximum 3-fold difference. These findings suggest that even when the Equivalent Rent Approach is improved, significant discrepancies remain.

Introduction

Housing price fluctuations exert effects on the economy through various channels. More precisely, however, relative prices between housing and other assets prices and goods/services prices are the variable that should be observed.

2013.10

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Abstract

The paper uses hedonic regression techniques in order to decompose the price of a house into land and structure components using real estate sales data for Tokyo. In order to get sensible results, a nonlinear regression model using data that covered multiple time periods was used. Collinearity between the amount of land and structure in each residential property leads to inaccurate estimates for the land and structure value of a property. This collinearity problem was solved by using exogenous information on the rate of growth of construction costs in Tokyo in order to get useful constant quality subindexes for the price of land and structures separately.

Introduction

In this paper, we will use hedonic regression techniques in order to construct a quarterly constant quality price index for the sales of residential properties in Tokyo for the years 2000-2010 (44 quarters in all). The usual application of a time dummy hedonic regression model to sales of houses does not lead to a decomposition of the sale price into a structure component and a land component. But such a decomposition is required for many purposes. Our paper will attempt to use hedonic regression techniques in order to provide such a decomposition for Tokyo house prices. Instead of entering characteristics into our regressions in a linear fashion, we enter them as piece-wise linear functions or spline functions to achieve greater flexibility.

2013.10

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Abstract

The paper studies the problems associated with the construction of price indexes for commercial properties that could be used in the System of National Accounts. Property price indexes are required for the stocks of commercial properties in the Balance Sheets of the country and related price indexes for the land and structure components of a commercial property are required in the Income Accounts of the country if the Multifactor Productivity of the Commercial Property Industry is calculated as part of the System of National accounts. The paper suggests a variant of the capitalization of the Net Operating Income approach to the construction of property price indexes and uses the one hoss shay or light bulb model of depreciation as a model of depreciation for the structure component of a commercial property.

Introduction

Many of the property price bubbles experienced during the 20th century were triggered by steep increases and sharp decreases in commercial property prices. Given this, there is a need to construct commercial property price indexes but exactly how should these prices be measured? Since commercial property is highly heterogeneous compared to housing and the number of transactions is also much lower, it is extremely difficult to capture trends in this market. In addition, many countries have been experiencing large investments in commercial properties and in countries where the market has matured, depreciation and investments in improvements and renovations represents a substantial fraction of national output. But clear measurement methods for the treatment of these expenditures in the System of National Accounts are lacking. Given this, one may say that the economic value of commercial property in particular is one of the indicators that is most difficult to measure on a day-to-day basis and that statistical development related to this is one of the fields that has perhaps lagged the furthest behind. Indexes based on transaction prices for commercial properties have begun to appear in recent years, especially in the U.S. However, in many cases, these indexes are based on property appraisal prices. But appraisal prices need to be based on a firm methodology. Thus in this paper, we will briefly review possible appraisal methodologies and then develop in more detail what we think is the most promising approach.

2013.10

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Abstract

We investigate the cross-sectional distribution of house prices in the Greater Tokyo Area for the period 1986 to 2009. We find that size-adjusted house prices follow a lognormal distribution except for the period of the housing bubble and its collapse in Tokyo, for which the price distribution has a substantially heavier right tail than that of a lognormal distribution. We also find that, during the bubble era, sharp price movements were concentrated in particular areas, and this spatial heterogeneity is the source of the fat upper tail. These findings suggest that, during a bubble period, prices go up prominently for particular properties, but not so much for other properties, and as a result, price inequality across properties increases. In other words, the defining property of real estate bubbles is not the rapid price hike itself but an increase in price dispersion. We argue that the shape of cross sectional house price distributions may contain information useful for the detection of housing bubbles.

Introduction

Property market developments are of increasing importance to practitioners and policymakers. The financial crises of the past two decades have illustrated just how critical the health of this sector can be for achieving financial stability. For example, the recent financial crisis in the United States in its early stages reared its head in the form of the subprime loan problem. Similarly, the financial crises in Japan and Scandinavia in the 1990s were all triggered by the collapse of bubbles in the real estate market. More recently, the rapid rise in real estate prices - often supported by a strong expansion in bank lending - in a number of emerging market economies has become a concern for policymakers. Given these experiences, it is critically important to analyze the relationship between property markets, finance, and financial crisis.

2013.04

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Abstract

We propose a new method to estimate quality adjusted commercial property price indexes using real estate investment trust (REIT) data. Our method is based on the present value approach, but the way the denominator (i.e., the discount rate) and the numerator (i.e., cash flows from properties) are estimated differs from the traditional method. We estimate the discount rate based on the share prices of REITs, which can be regarded as the stock market’s valuation of the set of properties owned by the REITs. As for the numerator, we use rental prices associated only with new rental contracts rather than those associated with all existing contracts. Using a dataset with prices and cash flows for about 500 commercial properties included in Japanese REITs for the period 2003 to 2010, we find that our price index signals turning points much earlier than an appraisal-based price index; specifically, our index peaks in the first quarter of 2007, while the appraisal-based price index exhibits a turnaround only in the third quarter of 2008. Our results suggest that the share prices of REITs provide useful information in constructing commercial property price indexes.

Introduction

Looking back at the history of economic crises, there are a considerable number of cases where a crisis was triggered by the collapse of real estate price bubbles. For example, it is widely accepted that the collapse of Japan’s land/stock price bubble in the early 1990s has played an important role in the subsequent economic stagnation, and in particular the banking crisis that started in the latter half of the 1990s. Similarly, the Nordic banking crisis in the early 1990s also occurred in tandem with a property bubble collapse, while the global financial crisis that began in the U.S. in 2008 and the recent European debt crisis were also triggered by the collapse of bubbles in the property and financial markets.

2013.02

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Abstract

In constructing a housing price index, one has to make at least two important choices. The first is the choice among alternative estimation methods. The second is the choice among different data sources of house prices. The choice of the dataset has been regarded as critically important from a practical viewpoint, but has not been discussed much in the literature. This study seeks to fill this gap by comparing the distributions of prices collected at different stages of the house buying/selling process, including (1) asking prices at which properties are initially listed in a magazine, (2) asking prices when an offer for a property is eventually made and the listing is removed from the magazine, (3) contract prices reported by realtors after mortgage approval, and (4) registry prices. These four prices are collected by different parties and recorded in different datasets. We find that there exist substantial differences between the distributions of the four prices, as well as between the distributions of house attributes. However, once quality differences are controlled for, only small differences remain between the different house price distributions. This suggests that prices collected at different stages of the house buying/selling process are still comparable, and therefore useful in constructing a house price index, as long as they are quality adjusted in an appropriate manner.

Introduction

In constructing a housing price index, one has to make several nontrivial choices. One of them is the choice among alternative estimation methods, such as repeatsales regression, hedonic regression, and so on. There are numerous papers on this issue, both theoretical and empirical. Shimizu et al. (2010), for example, conduct a statistical comparison of several alternative estimation methods using Japanese data. However, there is another important issue which has not been discussed much in the literature, but has been regarded as critically important from a practical viewpoint: the choice among different data sources for housing prices. There are several types of datasets for housing prices: datasets collected by real estate agencies and associations; datasets provided by mortgage lenders; datasets provided by government departments or institutions; and datasets gathered and provided by newspapers, magazines, and websites. Needless to say, different datasets contain different types of prices, including sellers’ asking prices, transactions prices, valuation prices, and so on.

2011.04

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Abstract

1990年代前半の日本のバブル崩壊期では住宅価格の大幅下落にもかかわらず家賃はほとんど変化しなかった。同様の現象はバブル崩壊後の米国でも観察されている。家賃はなぜ変化しないのか。なぜ住宅価格と家賃は連動しないのか。本稿では，こうした疑問に答えるため，大手住宅管理会社により提供された約 15,000 戸の家賃データを用いて分析を行い，以下の結果を得た。第 1 に，家賃が変更される住戸の割合は 1 年間で約 5%に過ぎないことがわかった。これは米国の 14 分の1，ドイツの 4 分の 1 であり，極端に低い。この高い硬直性の背景には，店子の入れ替えが少ない一方，家賃の契約期間が 2 年と長いため，そもそも家賃を変更する機会が限定されているという日本の住宅市場に特有の事情がある。しかしそれ以上に重要なのは，店子の入れ替えや契約更新など家賃変更の機会が訪れても家賃を変更していないということであり，これが家賃の変更確率を大きく引き下げている。店子の入れ替え時においては 76％の住戸で以前と同じ家賃が適用されており，契約更新の際には 97％の住戸で家賃が据え置かれている。第 2 に，Caballeroand Engel (2007)によって提案された Adjustment hazard function の手法を用いた分析の結果，各住戸の家賃が変更されるか否かは，その住戸の現行家賃が市場実勢からどの程度乖離しているかにほとんど依存しないことがわかった。つまり，家賃改定は状態依存ではなく時間依存であり，カルボ型モデルで描写できる。

Introduction

多くの先進主要国においては，住宅価格を中心とした資産価格の急激な上昇とその後の下落が，金融システムに対して甚大な影響をもたらすことで経済活動の停滞を招いた共通の歴史を持つ。1990 年代の日本・スウェーデン，そして，今回の米国のサブプライム問題に端を発した金融危機が，最も代表的な事例としてあげることができる。Reinhart andRogoff (2008)では，多くの国の経済データを網羅的かつ長期の時系列で比較分析し，金融危機がもたらされる背後には，多くの共通する経済現象が発生していることを明らかにした。その一つの事象が，資産価格，なかでも不動産価格が，賃貸料と比較して大きく上昇していることを指摘した。

2011.01

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Abstract

Do indexes of house prices behave differently depending on the estimation method? If so, to what extent? To address these questions, we use a unique dataset that we compiled from individual listings in a widely circulated real estate advertisement magazine. The dataset contains more than 470,000 listings of housing prices between 1986 and 2008, including the period of the housing bubble and its burst. We find that there exists a substantial discrepancy in terms of turning points between hedonic and repeat sales indexes, even though the hedonic index is adjusted for structural changes and the repeat sales index is adjusted in the way Case and Shiller suggested. Specifically, the repeat sales measure signals turning points later than the hedonic measure: for example, the hedonic measure of condominium prices bottomed out at the beginning of 2002, while the corresponding repeat sales measure exhibits a reversal only in the spring of 2004. This discrepancy cannot be fully removed even if we adjust the repeat sales index for depreciation.

Introduction

Fluctuations in real estate prices have a substantial impact on economic activity. In Japan, the sharp rise in real estate prices during the latter half of the 1980s and their decline in the early 1990s have led to a decade-long, or even longer, stagnation of the economy. More recently, the rapid rise in housing prices and their reversal in the United States have triggered a global financial crisis. Against this background, having a reliable index that correctly identifies trends in housing prices is of utmost importance.

2010.08

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Abstract

Is the cross-sectional distribution of house prices close to a (log)normal distribution, as is often assumed in empirical studies on house price indexes? How does the distribution evolve over time? To address these questions, we investigate the cross-sectional distribution of house prices in the Greater Tokyo Area. We find that house prices (P_i) are distributed with much fatter tails than a lognormal distribution and that the tail is quite close to that of a power-law distribution. We also find that house sizes (S_i) follow an exponential distribution. These findings imply that size-adjusted house prices, defined by lnP_i − aS_i, should be normally distributed. We find that this is indeed the case for most of the sample period, but not the bubble era, during which the price distribution has a fat upper tail even after adjusting for size. The bubble was concentrated in particular areas in Tokyo, and this is the source of the fat upper tail.

Introduction

Researchers on house prices typically start their analysis by producing a time series of the mean of prices across different housing units in a particular region by, for example, running a hedonic or repeat-sales regression. In this paper, we pursue an alternative research strategy: we look at the entire distribution of house prices across housing units in a particular region at a particular point of time and then investigate the evolution of such cross-sectional distribution over time. We seek to describe price dynamics in the housing market not merely by changes in the mean but by changes in some key parameters that fully characterize the entire cross-sectional price distribution.

2010.08

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Abstract

Is the cross-sectional distribution of house prices close to a (log)normal distribution, as is often assumed in empirical studies on house price indexes? How does it evolve over time? How does it look like during the period of housing bubbles? To address these questions, we investigate the cross-secional distribution of house prices in the Greater Tokyo Area. Using a unique dataset containing individual listings in a widely circulated real estate advertisement magazine in 1986 to 2009, we find the following. First, the house price, P_it, is characterized by a distribution with much fatter tails than a lognormal distribution, and the tail part is quite close to that of a power-law or a Pareto distribution. Second, the size of a house, S_i, follows an exponential distribution. These two findings about the distributions of P_it and S_i imply that the the price distribution conditional on the house size, i.e., Pr(P_it | S_i), follows a lognormal distribution. We confirm this by showing that size adjusted prices indeed follow a lognormal distribution, except for periods of the housing bubble in Tokyo when the price distribution remains asymmetric and skewed to the right even after controlling for the size effect.

Introduction

Researches on house prices typically start by producing a time series of the mean of prices across housing units in a particular region by, for example, running a hedonic regression or by adopting a repeat-sales method. In this paper, we propose an alternative research strategy: we look at the entire distribution of house prices across housing units in a particular region at a particular point of time, and then investigate the evolution of such cross sectional distributions over time. We seek to describe price dynamics in a housing market not merely by changes in the mean but by changes in some key parameters that fully characterize the entire cross sectional price distribution. Our ultimate goal is to produce a new housing price index based on these key parameters.

2010.04

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Abstract

An economic indicator faces two requirements. It should be timely reported and should not significantly be altered afterward to avoid erroneous messages. At the same time they should reflect changing market conditions constantly and appropriately. These requirements are particularly challenging for housing price indices, since housing markets are subject to large temporal/seasonal changes and occasional structural changes. In this study we estimate a hedonic price index of previously-owned condominiums of Tokyo 23 Wards from 1986 through 2006, taking account of seasonal sample selection biases and structural changes in a way it enables us to report the indexes timely which are not subject to change after reporting. Specifically, we propose an overlapping-period hedonic model (OPHM), in which a hedonic price index is calculated every month based on data in the “window” of a year ending this month (this month and previous eleven months). We also estimate hedonic housing price indexes under alternative assumptions: (i) no structural change (“structurally restricted”) and (ii) different structure for every month (“structurally unrestricted”). Results suggest that the structure of the housing market, including seasonality, changes over time, and these changes occur continuously over time. It is also demonstrated that structurally restricted indices that do not account for structural changes involve a large time lag compared with indices that do account for structural changes during periods with significant price fluctuations.

Introduction

Japan, the United States, and most advanced nations have experienced housing bubbles and subsequent collapses of the bubbles in succession. Recently, much attention has been focused on housing price indices. In macroeconomic policy, housing price indices are considered to be a possible candidate of “early warning signals” of sometimes devastating financial bubbles. In microeconomic spheres, there are growing needs for hedging against volatility in housing markets, and housing price indices may be used as a means of index trades.

2010.03

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Abstract

Purpose: When Japan’s asset bubble burst, the office vacancy rate soared sharply. This study targets the office market in Tokyo’s 23 special wards during Japan’s bubble burst period. It aims to define economic conditions for the redevelopment/conversion of offices into housing and estimate the redevelopment/conversion probability under the conditions.
Design/methodology/approach: The precondition for land-use conversion is that subsequent profit excluding destruction and reconstruction costs is estimated to increase from the present level for existing buildings. We estimated hedonic functions for offices and housing, computed profit gaps for approximately 40,000 buildings used for offices in 1991, and projected how the profit gaps would influence the land-use conversion probability. Specifically, we used panel data for two time points in the 1990s to examine the significance of redevelopment/conversion conditions.
Findings: We found that if random effects are used to control for individual characteristics of buildings, the redevelopment probability rises significantly when profit from land after redevelopment is expected to exceed that from present land uses. This increase is larger in the central part of a city.
Research limitations/implications: Limitations stem from the nature of Japanese data limited to the conversion of offices into housing. In the future, we may develop a model to generalize land-use conversion conditions.
Originality/value: This is the first study to specify the process of land-use adjustments that emerged during the bubble burst. This is also the first empirical study using panel data to analyse conditions for redevelopment.
Key words: hedonic approach, random probit model, urban redevelopment, Japan’s asset bubble Paper type: Research paper

Introduction

Sharp real estate price hikes and declines, or the formation and bursting of real estate bubbles, have brought about serious economic problems in many countries.

2010.01

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Abstract

Japan and the United States have experienced the housing bubbles and subsequent collapses of the bubbles in succession. In this paper, these two bubbles are compared and the following findings are obtained.
Firstly, upon applying twenty years of past data from Japan to the “repeat-sales method” and the “hedonic pricing method”, which are representative methods for calculating house prices, it was found that the timing at which prices bottomed out after the collapses of the bubbles differed depending on the two methods. The timing for bottoming out as estimated by the repeat-sales method delayed when compared to the estimate using the hedonic pricing method, by 13 months for condominiums and by three months for single-family homes. This delay is caused by the depreciation effect of building not being processed appropriately by the repeat-sales method. In the United States, the S&P/Case-Shiller Home Price Indices are representative house prices indices, which use the repeat-sales method. Therefore, it is possible that the timing for bottoming out is estimated to be delayed. As there are increasing interests in the timing for bottoming out of the US housing market, there is a risk that the existence of such a lag in cognition causes the increase of uncertainty and the delay in economic recovery.
Secondly, when looking at the relationship between the demand for houses and house prices based on the time-series data, there is a positive correlation between the two elements. However, upon conducting an analysis using the panel data, which is based on data in units of prefectures or states, there is no significant relationship between the demand for houses and house prices in both Japan and the United States. In this sense, it is hard to explain whether there is a bubble and the size of the bubble according to prefecture (state) using demand elements. This suggests that it is possible that the concept of demographics having an impact on the demand for houses, which thus caused the house prices to increase, is not effective in explaining the price fluctuations in neither Japan nor the United States.
Thirdly, when looking at the co-movement between the house prices and rent, a phenomenon which the rent almost does not fluctuate at all even when the significant change of house prices change in the process of the formation and collapse of a bubble was confirmed for both Japan and the United States. Its background is that landlords and tenants have formed long-term contractual relationships so that both parties can save on various transactional costs. In addition, the imputed rent of one’s home is not assessed using market prices in Japan, which is an aspect to weaken the co-movement. A lack of co-movement causes a phenomenon in Japan and the United States where consumer prices that include this rent as an important element do not increase since rent does not increase even if housing prices increase during a bubble period. Thus, it results in a delay towards a shift to tighten credits. Since rent prices do not move together with the house prices even after house prices decrease after the collapse of the bubble, a phenomenon which consumer prices do not decrease was observed. This served as a factor for the delay in a shift towards monetary relaxation. Rent prices are an important variable that serves as a node between asset prices and prices of goods and services. It is necessary to increase the accuracy with which it is measured.

Introduction

This paper’s objective is to find similarities and differences between the Japanese and US housing markets by comparing Japan’s largest postwar real estate bubbles in the 1980s and U.S. housing bubbles since 2000 that have reportedly caused the worst financial crisis since the 1929 Great Depression. While various points have been made about the housing bubbles, this paper attempts to specify the following points.

2010.01

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Abstract

Do the indexes of house prices behave differently depending on the estimationmethods? If so, to what extent? To address these questions, we use a unique datasetthat we have compiled from individual listings in a widely circulated real estateadvertisement magazine. The dataset contains more than 400 thousand listingsof housing prices in 1986 to 2008, including the period of housing bubble andits burst. We find that there exists a substantial discrepancy in terms of turningpoints between hedonic and repeat sales indexes, even though the hedonic indexis adjusted for structural change and the repeat sales index is adjusted in a wayCase and Shiller suggested. Specifically, the repeat sales measure tends to exhibita delayed turn compared with the hedonic measure; for example, the hedonicmeasure of condominium prices hit bottom at the beginning of 2002, while thecorresponding repeat-sales measure exhibits reversal only in the spring of 2004.Such a discrepancy cannot be fully removed even if we adjust the repeat salesindex for depreciation (age effects).

Introduction

Fluctuations in real estate prices have substantial impacts on economic activities. InJapan, a sharp rise in real estate prices during the latter half of the 1980s and its declinein the early 1990s has led to a decade-long stagnation of the Japanese economy.More recently, a rapid rise in housing prices and its reversal in the United States havetriggered a global financial crisis. In such circumstances, the development of appropriateindexes that allow one to capture changes in real estate prices with precision isextremely important not only for policy makers but also for market participants whoare looking for the time when housing prices hit bottom.

2009.11

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Abstract

Do the indices of house prices and rents behave differently depending on the estimation methods? If so, to what extent? To address these questions, we use a unique dataset that we have compiled from individual listings in a widely circulated real estate advertisement magazine. The dataset contains more than 400 thousand listings of housing prices and about one million listings of housing rents, both from 1986 to 2008, including the period of housing bubble and its burst. We find that there exists a substantial discrepancy in terms of turning points between hedonic and repeat sales indices, even though the hedonic index is adjusted for structural change and the repeat sales index is adjusted in a way Case and Shiller suggested. Specifically, the repeat sales measure tends to exhibit a delayed turn compared with the hedonic measure; for example, the hedonic measure of condominium prices hit bottom at the beginning of 2002, while the corresponding repeat-sales measure exhibits reversal only in the spring of 2004. Such a discrepancy cannot be fully removed even if we adjust the repeat sales index for depreciation (age effects).

Introduction

Fluctuations in real estate prices have substantial impacts on economic activities. In Japan, a sharp rise in real estate prices during the latter half of the 1980s and its decline in the early 1990s has led to a decade-long stagnation of the Japanese economy. More recently, a rapid rise in housing prices and its reversal in the United States have triggered a global financial crisis. In such circumstances, the development of appropriate indices that allow one to capture changes in real estate prices with precision is extremely important not only for policy makers but also for market participants who are looking for the time when housing prices hit bottom.

2009.05

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Abstract

日本と米国は相次いで住宅バブルとその崩壊を経験した。本稿ではこの 2 つのバブルを比較し以下のファインディングを得た。

第 1 に，住宅価格の代表的な計測手法である「リピートセールス法」と「ヘドニック法」をわが国の過去 20 年間のデータに適用した結果，バブル崩壊後の底入れの時期が 2 つの方法で異なることがわかった。リピートセールス法で推計される底入れ時期はヘドニック法の推計に比べマンションで 13 ヶ月，戸建てで 3 ヶ月遅れている。この遅れはリピートセールス法が建物の築年減価を適切に処理できていないために生じるものである。米国ではS&P/Case-Shiller 指数が代表的な住宅価格指数であるがこれはリピートセールス法を用いており，底入れ時期を遅く見積もる可能性がある。米国住宅市場の底入れの時期に関心が集まっている状況下，こうした認知ラグの存在は不確実性を増加させ経済の回復を遅らせる危険がある。

第 2 に，住宅需要と住宅価格の関係を時系列データでみると両者の間には正の相関がある。しかし県あるいは州単位のデータを用いてクロスセクションでみると，日米ともに両者の間に有意な相関は見られない。この意味で，バブルの県（州）別の有無または大小を需要要因で説明することはできない。人口動態が住宅需要に影響を及ぼしそれが住宅価格を押し上げるというストーリーは少なくともバブル期の価格上昇を説明する上では有効でない可能性を示唆している。

第 3 に，住宅価格と家賃の連動性をみると，バブルの形成・崩壊の過程で住宅価格が大きく変動しても家賃はほとんど動かないという現象が日米ともに確認できる。この背景には，家主と店子の双方が様々な取引コストを節約するために長期的な契約関係を結んでいることが挙げられる。また，日本については，持ち家の帰属家賃が市場価格で評価されておらず，それが連動性を弱めている面もある。連動性の欠如は，バブル期に住宅価格が上昇しても家賃が上昇しないためその家賃を重要な要素として含む消費者物価が上昇しないという現象を日米で生み，それが金融引き締めへの転換を遅らせた。また，バブル崩壊後は，住宅価格が下落しても家賃が連動しないため消費者物価が下落しないという現象が見られ，これは金融緩和への転換を遅らせる原因となった。家賃は資産価格と財サービス価格の結節点となる重要な変数であり，その計測精度を高める必要がある。

Introduction

本稿の目的は，戦後のもっとも大きな不動産バブルといわれた 1980 年代の日本と，1929年の世界大恐慌以来の金融危機をもたらした原因であるといわれる2000年以降の米国の住宅バブルを比較することで，その両市場の共通点と相違点を浮き彫りにすることである。住宅バブルに関して様々なことが指摘される中で，本稿では，特に，以下の点を明らかにすることを目的とした。

2009.03

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Abstract

Why was the Japanese consumer price index for rents so stable even during the period of housing bubble in the 1980s? In addressing this question, we start from the analysis of microeconomic rigidity and then investigate its implications about aggregate price dynamics. We find that ninety percent of the units in our dataset had no change in rents per year, indicating that rent stickiness is three times as high as in the US. We also find that the probability of rent adjustment depends little on the deviation of the actual rent from its target level, suggesting that rent adjustments are not state dependent but time dependent. These two results indicate that both intensive and extensive margins of rent adjustments are very small, thus yielding a slow reponse of the CPI to aggregate shocks. We show that the CPI inflation rate would have been higher by one percentage point during the bubble period, and lower by more than one percentage point during the period of bubble bursting, if the Japanese housing rents were as flexible as in the US.

Introduction

Fluctuations in real estate prices have substantial impacts on economic activities. For example, land and house prices in Japan exhibited a sharp rise in the latter half of the 1980s, and its rapid reversal in the early 1990s. This wild swing led to a significant deterioration of the balance sheets of firms, especially those of financial firms, thereby causing a decade-long stagnation of the economy. Another recent example is the U.S. housing market bubble, which started somewhere around 2000 and is now in the middle of collapsing. These recent episodes have rekindled researchers’ interest on housing bubbles.

2008.12

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