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The purpose of this paper is to decompose and measure the microstructure of property investment returns for Tokyo’s residential property markets in as much detail as possible in comparison with office market.

Using enterprise value data for property investment trust companies composed of share prices available on capital markets, this study proposed a method of estimating property investment returns corresponding to changes in capital markets, and clarified the distortion in capitalization rate that are formed based on property appraisal prices.

The results for residential property showed that as building floor space increased, income and price increased while the discount rate decreased. In particular, a higher return could be obtained from office property than residential property by investing in larger-scale properties. Building age lowered asset price and income for both residential and office property, especially for residential property.

In Japan, investors believe that investment returns are high for properties close to the city centre, relatively new properties and those with large design or floor space. Therefore, this study first measured how asset prices, income and asset price–income ratios that comprise property investment returns change based on differences in these property characteristics. Second, the reliability/distortion of information that can be observed on the property investment market was measured. Furthermore, there was a significant divergence between discount rates and risk premiums formed by asset or space markets versus capital markets.

The differences of discount rate and risk premium formed by asset markets versus capital markets indicate that appraisal prices have biases. Thus, when it comes to property investment decisions, it is essential to make active use not just of property investment returns based on appraisal prices formed by asset markets but also information formed by capital markets.

A greater difference was generated in a shrinking market, suggesting that analysing property returns estimated on asset market information alone could lead to erroneous investment decisions.

This research is the first to use the enterprise value data from real estate investment trust companies composed of share prices available on capital markets for calculating discount rate and risk premium in property market.

While consumers did not previously have information on detailed housing features via traditional media, such as magazines, nowadays, due to the progress in information technology, they can access detailed information on various housing features via housing information websites. Therefore, detailed housing features may affect current rents to some extent. This paper aims to identify the effects of detailed housing features on rent and on omitted variable bias in Tokyo, Japan.

This paper applies the hedonic approach. To identify the effects of features which are not observed previously, we use a unique data set that contains various housing features and over 200,000 housing units. This data set enables to simulate the situations when the researcher cannot get some variables, and this simulation shows which variables cause omitted variable bias.

The analysis shows that housing features significantly influence housing rent. If significant housing feature variables are not included in the hedonic model, the estimated coefficients show omitted variable bias. Additionally, unit-specific features such auto-locking door can cause omitted variable bias on location-specific features such accessibility to downtown.

This paper shows empirical evidence that detailed housing features can cause omitted variable bias on other features including variables which are often used in previous searches. The result from our unique data set can be a guide for variable selection to reduce omitted variable bias.

As interest grows in environmentally friendly buildings, or “green buildings,” the real estate industry is expected to play an increasingly active role in the realization of a low-carbon society. Various efforts toward such a society are currently being promoted vigorously within an international framework. To supply a socially desirable level of green buildings via the market mechanism, the economic value of green buildings (as measured by the market) must be commensurate with the required investment. Many remain sceptical, however, about the true economic value of green buildings. A thorough analysis has yet to be conducted to evaluate whether green buildings realize income increases commensurate with the enormous initial investments required, although it is clear that cost savings do result from lower energy consumption. This paper shows through a series of analyses that certain market conditions must be in place in order for green buildings to produce economic value. Specifically, it used the hedonic approach to clarify whether or not there is added economic value, focusing on the new condominium market in the Tokyo metropolitan area. Based on a demonstration analysis of the housing market, the author shows that new condominiums with “green labels” using “sustainable measures” command a premium of approximately 5.8% in asking prices and 4.7% in transaction prices.

This study aims to explore the spatial externalities of apartment vacancy rates on housing rent by considering multiple vacancy durations.

This research uses smart meter data to measure unobservable vacant houses. This study made a significant contribution by applying building-level smart meter data to housing market analysis. It examined whether vacancy duration significantly affected apartment rent and whether the relationship between apartment rent and vacancy rate differed depending on the level of housing rent.

The primary finding indicates that there is a significant negative correlation between apartment rent and vacancy duration. Considering the spatial externalities of apartment vacancy rates, the apartment vacancy rates of surrounding buildings did not show any statistical significance. Moreover, quantile regression results indicate that although the bottom 10% of apartment rent levels showed a negative correlation with all vacancy durations, the top 10% showed no statistical significance related to vacancies.

This study measures the extent of spatial externalities that can differentiate taxation based on housing vacancies.

The findings indicate that landlords have asymmetric information about their buildings compared with the surrounding buildings, and the extent to which price adjusts for long-term vacancies differs depending on the level of apartment rent.

This study aims to investigate the relationship between market share and rent levels to understand the supply structure in the Japanese private rental housing market.

The study calculates the municipal-level market share of a dominant rental housing operator in Japan and ascertained the overall market rent and the dominant operator’s rent premium at the municipal level by using a major web portal’s listing data of rental houses.

The study shows that, as market share increased, overall market rent tends to decrease, and analyzed by market share, there is no significant difference between the rent of the dominant operator and the overall market rent.

The results of the study suggest that dominant operators may have lowered the rent of their own property to prioritize filling vacancies, which, in turn, causes the overall level of market rent to decline. This is an outcome of rental housing operators’ strategy to maximize long-term rental income under sublease contracts with individual owners, which ensures stable rental income for owners regardless of the occupation status of the apartments.

Previous research on regional monopolies in mortgage sales and brokerage businesses in the USA implies that rental housing operators in a position of great influence over the market can control and keep the market rents at high levels, that is, at large costs for consumers. The findings of the study are novel in showing the inverse relationship in the Japanese private rental market.

Houses are durable, so an imbalance between demand and supply occurs after time has passed since initial construction. The purpose of this study is to quantify the extent of this imbalance for existing houses, focusing on the heterogeneity across property segments.

This study uses a unique data set on the “inquiry volume” that each property received from an online real estate portal to measure the volume of demand in relation to supply. Simple regressions are conducted in the resale condominium market across the Tokyo metropolitan area.

The inquiry volume successfully tracked a recent expected trend in which demand relative to supply is stronger for condominiums in reasonably priced areas, condominiums in convenient, accessible locations, condominiums built within the last 20 years and compact and spacious units. This study also confirms that these trends cannot be captured through heterogeneity in price levels, which has been widely used in previous studies on measuring housing preferences.

As an indicator of conditions in the housing market, the property-level inquiry volume has strong potential to provide useful information for supply strategies and for the sustainable use of existing housing stocks.

The originality of this paper is the use of information on the buyer side, which is typically unobservable.

An economic indicator faces two requirements. It should be reported in a timely manner and should not be significantly altered afterward to avoid erroneous messages. At the same time, it 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. The purpose of this paper is to estimate a hedonic price index of condominiums of Tokyo, taking account of seasonal sample selection biases and structural changes in a way it enables us to report the index in a manner which is timely and not subject to change after reporting.

The paper proposes 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 11 months). It also estimates standard hedonic housing price indexes under alternative assumptions: no structural change (“structurally restricted”: restricted hedonic model) and different structure for every month (“structurally unrestricted”: unrestricted hedonic model).

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.

Following the financial crisis triggered by the US housing market, housing price index guidelines are currently being developed, with the United Nations, International Monetary Fund, and Organization for Economic Co‐operation and Development leading the way. These guidelines recommend that indices be estimated based on the hedonic method. We believe that the hedonic method proposed here will serve as a reference for countries that develop hedonic method‐based housing price indices in future.

In the many studies involving conventional housing price indices, whether those using the repeat‐sales method or hedonic method, there are few that have analyzed the problem of market structural changes. This paper is the first to construct a large database and systematically estimate the effect that changes in market structure have on housing price indices.

The purpose of this article, starting from linear regression, was to estimate a switching regression model, nonparametric model and generalized additive model as a semi-parametric model, perform function estimation with multiple nonlinear estimation methods and conduct comparative analysis of their predictive accuracy. The theoretical importance of estimating hedonic functions using a nonlinear function form has been pointed out in ample previous research (e.g. Heckman et al. (2010).

The distinctive features of this study include not only our estimation of multiple nonlinear model function forms but also the method of verifying predictive accuracy. Using out-of-sample testing, we predicted and verified predictive accuracy by performing random sampling 500 times without replacement for 9,682 data items (the same number used in model estimation), based on data for the years before and after the year used for model estimation.

As a result of estimating multiple models, we believe that when it comes to hedonic function estimation, nonlinear models are superior based on the strength of predictive accuracy viewed in statistical terms and on graphic comparisons. However, when we examined predictive accuracy using out-of-sample testing, we found that the predictive accuracy was inferior to linear models for all nonlinear models.

In terms of the reason why the predictive accuracy was inferior, it is possible that there was an overfitting in the function estimation. Because this research was conducted for a specific period of time, it needs to be developed by expanding it to multiple periods over which the market fluctuates dynamically and conducting further analysis.

Many studies compare predictive accuracy by separating the estimation model and verification model using data at the same point in time. However, when attempting practical application for auto-appraisal systems and the like, it is necessary to estimate a model using past data and make predictions with respect to current transactions. It is possible to apply this study to auto-appraisal systems.

It is recognized that housing price fluctuations caused by the subprime crisis had a massive impact on the financial system. The findings of this study are expected to serve as a tool for measuring housing price fluctuation risks in the financial system.

While the importance of nonlinear estimation when estimating hedonic functions has been pointed out in theoretical terms, there is a noticeable lag when it comes to testing based on actual data. Given this, we believe that our verification of nonlinear estimation’s validity using multiple nonlinear models is significant not just from an academic perspective – it may also have practical applications.

Aging in Japan is advancing faster than in other major developed nations, and this is expected to have substantial effects on the country’s economic systems, including its social security system. What kind of effect will the falling birth rate, aging society and declining population have on the real estate market? Will the often mentioned real estate price asset meltdown really occur? The purpose of this paper is to address these questions by investigating how much demographic factors affected real estate prices in Japan and the USA.

The authors use regional panel data for Japan and the USA real estate prices and estimate the effects of demographic factors, such as dependency ratio, i.e. the ratio of population aged 65+ to population aged 20-64. For Japan, as no region-by-region quality-adjusted housing price indexes covering the entire country exist, data are constructed by conducting quality adjustment using hedonic regression.

Both in Japan and the USA, real estate prices in a region are inversely correlated with the old age dependency ratio in that region, and positively correlated with the total number of population in that region. The demographic factor had a greater impact on real estate prices in Japan than in the USA. For Japan, it was also found that demographic impact on land prices will be −2.4 per cent per year in 2012-2040, while it was −3.7 per cent per year in 1976-2010, suggesting that aging will continue to have downward pressure on land prices over the next 30 years, although the demographic impact will be slightly smaller than it was in 1976-2010, as the old age dependency ratio will not increase as much as it did before.

Japan’s regional panel data are newly constructed based on a hedonic approach. Analyzing the effect of dependency ratio for Japan and the USA panel data is a new challenge. Forecasting future impact of demographic factor on Japan’s land prices based on the population forecast is a new challenge.

This paper seeks to investigate the nature and magnitude of the distortion in appraisal land price information according to change in the market, with a special focus on the Government's Published Land Prices.

In Japan, there is an item of land price information, so‐called Koji‐Chika (PLPS: Published Land Price Information System), that is a survey of fair market value by the qualified appraisers. The valuation error of this land price information was analyzed using the following method. First, hedonic price indices were constructed based on both actual transaction prices and the Published Land Prices, they were then compared to detect possible distortions in the governmental price information. Also the possibility of structural change in the Japanese real estate markets was studied and its effect on price indices was considered. Analysis of the Tokyo metropolitan area in Japan took place between 1975 and 1999

Large and systematic discrepancies between actual transaction prices and the Published Land Prices were identified, which might suggest that there are serious problems in the governmental information system. It is believed that it is necessary to consider this issue in the context of the entire real estate appraisal system in Japan.

Limitations stem from the nature of Japanese data. Future research will seek to look at values on an IPD index.

The land market in Tokyo experienced a so‐called Bubble economy, and the rapid rise and fall of the land price were generated for this period.