Launch of New Service Integrating 'Walkability Index' with AI Real Estate Valuation and Price Map

PriceHubble Japan (hereafter 'PriceHubble') and Nikken Sekkei Research Institute (hereafter 'NSRI') have commenced the development and provision of a new service integrating NSRI's 'Walkability Index' with PriceHubble's AI real estate valuation and 'Price Map.' By simultaneously visualizing 'accessibility of facilities within walking distance (livability)' and 'real estate prices/market trends,' this service aims to enhance the sophistication and transparency of property valuation, supporting more confident decision-making.

Overview of the New Service

The service provides metrics and visualization tools to evaluate areas comprehensively based on both livability, measured by walking distance to facilities, and market price levels estimated via AI. It is designed for purposes such as: 1) exploring 'hidden gem' areas where market prices are relatively low compared to livability; and 2) identifying whether market price increases are backed by actual demand driven by improved livability.

Note: Covers Nakano and Suginami wards. Areas near stations along the Chuo Line tend to have high levels of both livability and price. Map data: © OpenStreetMap contributors

<Usage Examples>

Walkability Index (Nikken Sekkei Research Institute) Quantifying facility density within walking distance from a livability perspective. NSRI, under the supervision of Professor Chihiro Shimizu of Hitotsubashi University's Graduate School of Social Data Science, developed the patented 'Walkability Index,' which quantifies 'ease of living on foot' from urban big data. This index evaluates, on a 100-point scale, the accumulation of 'urban amenities'—essential daily facilities, commercial/leisure facilities, and educational facilities—accessible by walking. It is calculated using data provided by Zenrin Co., Ltd. and urban open data.

AI Real Estate Valuation / Price Map (PriceHubble) Predicting property prices and visualizing the rationale (price differences based on location). PriceHubble's AI real estate valuation estimates fair market prices by utilizing machine learning on diverse data, including historical listings, market trends, property attributes, and environmental data. It adopts a highly interpretable approach that clearly explains how factors like location and property specs impact prices.

'Price Map' utilizes these features to visualize price variations based purely on location as a heatmap. It allows for intuitive understanding of price levels and area characteristics at each location, effectively supporting area selection and pricing strategy development.

Future Prospects

While already in use for area selection, the companies plan to continue improving and expanding the service's functions to provide greater value to real estate agents, housing developers, and financial institutions. Through this initiative, both companies aim to contribute to more advanced data utilization and increased transparency in the real estate market.