7. New Estimates for the Price of Housing in the Japanese CPI

Others that have been pointed out include (a) a relative rise in debt compared to income and net assets and an increase in leveraging, (b) a sustained influx of capital and (c) a lag in productivity increases compared to increases in asset values and debt.

This is the so-called user cost and is calculated based on payments (including mortgage interest) and ownership-based taxes (fixed asset tax, etc.).

Housing services represent 26.4% of the CPI for Tokyo’s wards. Of this, 4.3% is private rent paid by tenants to owners while the remaining 19.4% is the imputed rent that the owner’s of dwelling units pay for the services of their units. These imputed rents are the rents that owners would pay if they rented a dwelling unit of similar quality; i.e., the rental equivalence approach is used to compute imputed rents in the Japanese CPI.

With regard to this point, refer to Goodhart (2001).

See Diewert (2015) and Shimizu, Diewert, Nishimura and Watanabe (2012).

Diewert (2007) argued that the correct opportunity cost for valuing the services of an owned dwelling unit is the maximum of the amount the unit could rent for in the current rental market and the user cost of the dwelling unit since this represents the financial opportunity cost of tying up ones capital in the dwelling. In most countries, the user cost of a high end home is often approximately twice as high as its rental equivalence price. For less expensive owned homes, the user cost is usually much closer to the amount it could rent for. However, the situation in Japan could be quite different since Japan has experienced widespread asset deflation which did not occur in other developed countries.

For example, according to the 2013 Housing and Land Survey, the average floor space (size) of owner-occupied housing in Tokyo was 110.64 square meters for single-family house owner-occupied housing and 82.71 square meters for rental housing—a discrepancy of over 30 square meters. For condominiums, an even greater discrepancy exists: the floor space is 65.73 square meters for owner-occupied housing and 37.64 square meters for rental housing. Moreover, in addition to the difference in floor space between rented and owned units, the quality of the owned units tends to be higher than the rented units and these quality differences need to be taken into account.

On the other hand, contract rent is the “right” rental concept to use to value the cost of rental housing for the renter and it is the correct price to use to calculate the rental income of the owner of the rental unit.

A possible explanation for this rent invariance result is that landlords hold rents constant even though the quality of the units deteriorates over time due to depreciation. Thus given that general inflation in Japan is zero or less, landlords are actually increasing constant quality rents by holding nominal prices constant. Thus landlords that are experiencing rollover or new contracts are happy enough to just charge the previous level of rents since they are actually achieving a hidden price increase in rents. Thus we do not expect this rental invariance result to hold in other countries that are not experiencing general deflation. However, it is also possible that this invariance result is due to quality adjustment bias; i.e., the above results are based on overall average prices and do not take into account quality differences between individual units. We will look at quality adjustment in Sect. 7.3 below.

This adds up to a total of around 28,000 households covered by the survey (see the 2013 Annual Report on the Retail Price Survey).

Rental survey areas are distributed by survey municipality stratification.

The most recent survey results are used for the rents of the groups that are not surveyed in a given month. In addition, if a surveyed unit becomes vacant, the most recent survey result will be used until it is next occupied. Moreover, samples are replaced by changing the rental survey areas every five years or so.

Housing with a total floor space of less than 30 m\(^{2}\) is considered small and housing with a total floor space of 30 m\(^{2}\) or more is considered medium. The comparative price per 3.3 m\(^{2}\) is obtained by dividing the total number of rental homes by the total floor space and multiplying by 3.3. Indexes by basic unit category are calculated by dividing the comparative value by the baseline price, then the rent index is obtained by calculating weighted averages for the indexes by category.

January 2010 was taken as the baseline point. For each of the four index types (small wooden housing, medium wooden housing, small non-wooden housing, and medium non-wooden housing), we divided the total rent by the total area and multiplied the result by 3.3 to obtain the comparison price per 3.3 m\(^{2}\). With regard to the total area categories, we divided them by taking 30 m\(^{2}\) as the reference value in the same manner as official indexes. For the housing structure, houses coded as “wooden” in the data were taken as wooden housing and all others were categorized as non-wooden housing. Indexes by category were calculated by dividing the comparative price by the baseline price. Since weights for aggregating the four category indexes have not been published, we calculated the weights based on the proportion of each category in Tokyo’s ward areas in the 2009 Housing and Land Survey, in the same manner as the imputed rent for owned homes.

In terms of the categories, formula and weighting, the same method as for the Tokyo ward area described above was used but not all payment information was used; instead, the calculations used only hypothetically sampled data based on the same method as the MIC.

The survey area size was based on the fact that the standard census survey area is set at around 50 households. We created 982 artificial survey areas.

For prices in the groups that were not surveyed in a given month, the data for the most recent applicable month were used in the same manner as the official index. Additionally, in cases where rent was no longer paid due to a tenant departure, the most recent price was used until new payment information was recorded based on the arrival of the next tenant.

The target rent \(R^{*}\) was estimated based on a hedonic method in the same manner as was used by Shimizu, Nishimura and Watanabe (2010a). With regard to the choice of hedonic model, we chose the Rolling Hedonic Model proposed by Shimizu, Ono, Takatsuji and Nishimura (2010).

Comparing the analysis results in Tables 7.5 to 7.3 in Shimizu, Nishimura and Watanabe (2010a), the value of \(\Lambda (x)\) differs considerably. According to Table 7.3 in Shimizu, Nishimura and Watanabe (2010a), the value of \(\Lambda (x)\) is around 10%, which is double the result in this paper. When we look at the source of this difference, we find that it can largely be explained by the difference in probability \(\text {Pr}(\Delta R_{it}\ne 0\,|\,I_{it}^{N}=1,x_{it}=x)\). In this paper, the probability that rent will be changed when there is tenant turnover is approximately 20%, but in Shimizu, Nishimura and Watanabe (2010a) it is around 70%. This may reflect a fundamental difference in the nature of the buildings covered by the analysis. It may also reflect the difference in the analysis periods. Specifically, Shimizu, Nishimura and Watanabe (2010a)’s sample period includes the bubble period. That was a time when major fluctuations in the market occurred rapidly, so we may consider that landlords and tenants both behaved in a way that led to rent being changed when a new contract was agreed.

The average of coefficient \(\beta \) for “Age of Building” in Table 7.2 is \(-0.011\). It means that the depreciation rate is minus 1.1% per year.

The depreciation rate is expressed as a fraction of property value, which includes the value of land. Thus the structure (net) depreciation rate is actually higher than 1.1% per year.

http://​epp.​eurostat.​ec.​europa.​eu/​portal/​page/​portal/​hicp/​methodology/​residential_​property_​price_​indices.