  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.
