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When Japan's asset bubble burst, the office vacancy rate soared sharply. This study seeks to target 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.

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. Regarding hedonic functions for offices and housing and computed profit gaps for approximately 40,000 buildings used for offices in 1991, it was projected how the profit gaps would influence the land‐use conversion probability. Specifically, panel data for two time points in the 1990s were used to examine the significance of redevelopment/conversion conditions.

It was 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.

Limitations stem from the nature of Japanese data limited to the conversion of offices into housing. In the future, a model may be developed to generalize land‐use conversion conditions.

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 analyze conditions for redevelopment.

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.