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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.

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.

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.