Housing rents usually correlate with factors such as the building’s age, facilities, and location. Yet not all rentals with similar physical factors charge the same rent. Psychological factors such as the subjective perceptions of the neighborhood matter as well.

Considering these perception variables, an Osaka Metropolitan University team has developed a method with almost 75% accuracy in explaining housing prices in Osaka City.

The team led by Graduate School of Human Life and Ecology student Xiaorui Wang and Professor Daisuke Matsushita used existing Osaka City property datasets and incorporated additional information on the physical factors (sky, vegetation, and buildings) of the streetscape images, and the impressions (safety, beauty, depression, liveliness, wealth, and boredom) of the streetscape using machine learning.

The method predicted rent prices with an accuracy of 73.92%. Among the variables, the neighborhood perceptions ranked highly as an indicator, just behind the building age, floor area, and distance to the central business district.

The findings were published in Habitat International.

Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Funding
This study was supported by JSPS KAKENHI [Grant Number 24K01053], and JST SPRING [Grant Number JPMJSP2139].

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