Kiyohiko G.NishimuraBack to index

  • Term Structure Models During the Global Financial Crisis: A Parsimonious Text Mining Approach

    Abstract

    This work develops and estimates a three-factor term structure model with explicit sentiment factors in a period including the global financial crisis, where market confidence was said to erode considerably. It utilizes a large text data of real time, relatively high-frequency market news and takes account of the difficulties in incorporating market sentiment into the models. To the best of our knowledge, this is the first attempt to use this category of data in term-structure models.  

    Although market sentiment or market confidence is often regarded as an important driver of asset markets, it is not explicitly incorporated in traditional empirical factor models for daily yield curve data because they are unobservable. To overcome this problem, we use a text mining approach to generate observable variables which are driven by otherwise unobservable sentiment factors. Then, applying the Monte Carlo filter as a filtering method in a state space Bayesian filtering approach, we estimate the dynamic stochastic structure of these latent factors from observable variables driven by these latent variables. 

    As a result, the three-factor model with text mining is able to distinguish (1) a spread-steepening factor which is driven by pessimists’ view and explaining the spreads related to ultra-long term yields from (2) a spread-flattening factor which is driven by optimists’ view and influencing the long and medium term spreads. Also, the three-factor model with text mining has better fitting to the observed yields than the model without text mining.

    Moreover, we collect market participants’ views about specific spreads in the term structure and find that the movement of the identified sentiment factors are consistent with the market participants’ views, and thus market sentiment.  

    Introduction

    Although “market sentiment” is often regarded as an important driver of asset markets,1 it is not explicitly incorporated in traditional empirical factor models for the term structure of interest rates. This is because (1) it is not clear what sentiment factors mean, and moreover, (2) there are scant observations, if any, about these sentiment factors. This work formulates and estimates a factor model with explicit sentiment factors in the period including the global financial crisis, in which uncertainty was said to be heightened considerably. It utilizes a large text data of real-time, relatively high-frequency market news and takes account of the difficulties (1) and (2). To the best of our knowledge, this is the first attempt to use this category of data in term-structure models.

     

    WP003

  • Estimating Quality Adjusted Commercial Property Price Indexes Using Japanese REIT Data

    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.

  • Multi-Belief Rational-Expectations Equilibria: Indeterminacy, Complexity and Sustained Deflation

    Abstract

    In this paper, we extend the concept of rational-expectations equilibrium, from a traditional single-belief framework to a multi-belief one. In the traditional framework of single belief, agents are supposed to know the equilibrium price “correctly.” We relax this requirement in the framework of multiple beliefs. While agents do not have to know the equilibrium price exactly, they must be correct in that it must be always contained in the support of each probability distribution they think possible. We call this equilibrium concept a multibelief rational-expectations equilibrium. We then show that such an equilibrium exists, that indeterminacy and complexity of equilibria can happen even when the degree of risk aversion is moderate and, in particular, that a decreasing price sequence can be an equilibrium. The last property is highlighted in a linear-utility example where any decreasing price sequence is a multi-belief rational-expectations equilibrium while only possible single-belief rational-expectations equilibrium price sequences are those which are constant over time.

    Introduction

    This paper considers a pure-endowment nonstochastic overlapping-generations economy. In this framework, we extend the concept of rational-expectations equilibrium, or in other words perfect-foresight equilibrium in our setting, in which generations in the model are supposed to know the equilibrium price “correctly.” Thus, there is no surprise in this rational-expectations equilibrium. We relax this requirement to the one that while generations do not know the equilibrium price exactly, they have a set of purely-subjective probability distributions of possible prices. In addition, they must not be surprised by the realization of the equlibrium price. That is, generations’ multi-belief expectations must be “correct” in that the equilibrium price is always contained in the support of each probability distribution they think possible. We call this equilibrium concept multi-belief rational-expectations equilibrium. Furthermore, the realization of the price which clears the market never disappoints generations’ expectations since they assign a positive (but possibly less than unity) probability to the occurrence of that price. Thus, their expectations are “realized.” Importantly, the generations’ beliefs are endogenously determined as a part of multi-belief rational-expectations equilibrium. This is similar to sequential equilibrium in an extensive-form game where the probability distribution at each information set is endogenously determined (although while a unique distribution is determined in a sequential equilibrium, a set of distributions is determined in ours). Obviously, single-belief rational-expectations equilibrium where generations’ expectations are singleton sets is ordinary rational-expectations equilibrium.

  • The Estimation of Owner Occupied Housing Indexes using the RPPI: The Case of Tokyo

    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.

  • Estimating Quality Adjusted Commercial Property Price Indexes Using Japanese REIT Data

    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.

  • House Prices at Different Stages of the Buying/Selling Process

    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.

  • Housing Prices in Tokyo: A Comparison of Hedonic and Repeat Sales Measures

    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.

  • Structural and Temporal Changes in the Housing Market and Hedonic Housing Price Indices

    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.

  • House Prices in Tokyo: A Comparison of Repeat-Sales and Hedonic Measures”

    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.

  • Housing Prices and Rents in Tokyo: A Comparison of Repeat-Sales and Hedonic Measures

    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.

  • Residential Rents and Price Rigidity: Micro Structure and Macro Consequences

    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.

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