1) Research on changes in the cross-sectional distribution of home prices
We will continue the research on changes in the distribution of home prices over time that we began in 2010. Research during 2011 will focus on differences in home price distribution during and outside bubbles.
2) Construction of a home price index that accounts for the time it takes until a sale is made
After the bubble in Japanese home prices burst, prices slowly declined for over 10 years. This was a factor in slowing the resolution of banks' bad loans and excessive corporate debt. Since the collapse of the bubble in the USA, a slow fall, the same trend as in Japan, has been seen. This research will examine why home prices fall slowly. Our hypothesis is that sellers dislike finalizing losses and thus set their asking prices high. This means that it takes a long time to sell homes. Eventually, however, buyers are found and thus the homes appear in data to have sold at fairly high prices. In other words, the market price for homes cannot be grasped merely by looking at agreed prices. It is also necessary to incorporate the time it takes until homes sell. In this research, we will obtain and analyze data on changes in home prices (data that records changes in asking prices from the first seller listing to the conclusion of a contract) from Recruit Co. and the Real Estate Information Network (REINS) for East Japan. The final result of our analysis will be the designing of a home price index that incorporates the amount of time it takes to sell homes. It will be presented to the Ministry of Land, Infrastructure and Transport's study group on improving the indexes for trends in real estate prices (of which Watanabe is a member). In addition, it will be presented at international conferences of real estate experts.
3) Comparative analysis of various home price databases
There are three types of raw data on home prices in Japan. They are 1) registry data (using registries to grasp when transactions have been made and surveying buyers about the price), 2) REINS data (an information exchange system for real estate marketers), and 3) Recruit data (property prices indicated on the Recruit Co. website). These datasets differ in terms of points such as the extent of the transactions they cover, the accuracy of the transaction prices they record, how accurately transaction timing is recorded, and how quickly transaction information is reflected. In this research, we will match properties included in all three datasets in order to clarify the characteristics of each dataset. Our research results will be reported to the Ministry of Land, Infrastructure and Transport's study group on improving the indexes for trends in real estate prices. They will be used as a resource for determining which datasets should be used when compiling a home price index. The results will be reported at an international conference in The Hague in spring 2011 (Workshop on Residential Property Price Indices, organized by Statistics Netherlands).