Estimation of redevelopment probability using panel data

Abstract

When Japan's asset bubble burst, the office vacancy rate soared sharply. This study targets 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. Design/methodology/approach: 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. We estimated hedonic functions for offices and housing, computed profit gaps for approximately 40,000 buildings used for offices in 1991, and projected how the profit gaps would influence the land-use conversion probability. Specifically, we used panel data for two time points in the 1990s to examine the significance of redevelopment/conversion conditions. Findings: We 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. Research limitations/implications: Limitations stem from the nature of Japanese data limited to the conversion of offices into housing. In the future, we may develop a model to generalize land-use conversion conditions. Originality/value: 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 analyse conditions for redevelopment.

Supply Management of Rental Housing Facilities: Effect of Changes in the Quality of Housing Equipment in the Tokyo Housing Rental Market

“…In terms of building equipment, the ancillary rate is over 50% for the bicycle parking lot and fiber optic Internet. The equipment for which there is a large difference in ancillary rate between old and new stocks (ancillary rate increased) is automatic entrance door (+65.0%), TV intercom (+55.4%), delivery locker 10 This result is consistent with the results of Shimizu et al [1]. 11 Although it seems that the housing equipment ancillary to the room and building fluctuates somewhat due to renewal and so on, the equipment seems to be influenced by the construction date.…”

“…In Japan, many houses were destroyed due to the large-scale air raids during the Second World War, not only in metropolitan areas but also in regional cities. In particular, a large number of houses were destroyed in the Tokyo metropolitan area, 1 and very-low-quality houses were built in a disorganized manner to satisfy the urgent housing demand in the chaotic postwar period. In the so-called high economic growth period that began in the mid-1950s, such houses were rapidly upgraded as large numbers of apartment buildings came up throughout Japan.…”

“…The transformation of Tokyo into an international financial center was a symbolic policy, and against this background, redevelopment rapidly advanced in major cities. Under such circumstances, the conversion of building use of even physically usable housing into offices, commercial facilities, and so on was promoted through rebuilding, and the advancement of land use was promoted [1].…”

Hedonic Analysis for the Estimation of Condominium Rent Utilizing Web Information

“…In this analysis, the rent for housing units in condominiums was analyzed. However, according to Shimizu et al (2010), variation in rents is smaller than that for housing prices. For example, the coeffi cient of variation (ie, the standard deviation divided by the average) for the condominium housing price is 0.066, while that for the condominium housing rent is 0.020 (both calculated on a monthly basis for 2005-10).…”

“…The original data are rent data for condominiums in twenty-three wards of Tokyo, provided by At Home Co Ltd. These rents are the fi nal list rents that were set by suppliers, and the discrepancy between the fi nal list rents and the actual rents is known to be small (Shimizu et al, 2010). The original data include the monthly rent of the housing unit (yen per month per m 2 ), the size of the housing unit (m 2 ) and its square, the distance to the nearest station (m), the number of the fl oor, the building age (years to 2009), and the time necessary to reach Tokyo or Shinjuku station (the principal terminals in Tokyo) (min).…”