The economic value of neighborhoods: Predicting real estate prices from the urban environment

Marco De Nadai, Bruno Lepri


Housing costs have a significant impact on individuals, families, businesses, and governments. Recently, online companies such as Zillow have developed proprietary systems that provide automated estimates of housing prices without the immediate need of professional appraisers. Yet, our understanding of what drives the value of houses is very limited. In this paper, we use multiple sources of data to entangle the economic contribution of the neighborhood's characteristics such as walkability and security perception. We also develop and release a framework able to now-cast housing prices from Open data, without the need for historical transactions. Experiments involving 70,000 houses in 8 Italian cities highlight that the neighborhood's vitality and walkability seem to drive more than 20% of the housing value. Moreover, the use of this information improves the nowcast by 60%. Hence, the use of property's surroundings' characteristics can be an invaluable resource to appraise the economic and social value of houses after neighborhood changes and, potentially, anticipate gentrification.

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author={M. {De Nadai} and B. {Lepri}},
booktitle={2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)},
title={The Economic Value of Neighborhoods: Predicting Real Estate Prices from the Urban Environment},
keywords={Internet;pricing;property market;real estate data processing;Web sites;neighborhood changes;predicting real estate prices;urban environment;housing costs;online companies;proprietary systems;housing prices;professional appraisers;economic contribution;security perception;Open data;housing value;Italian cities highlight;Urban areas;Companies;Security;Economics;Industries;Local government;Data models;urban science;automated real estate;multimodal features},