This study addresses the issue of how modern information systems suchas the geographic information system can help investment and managerialdecisions on real estate location. Specifically, this work is a case study of thecity of Manchester in the United Kingdom. Thematic maps are created forfour districts within Manchester using socio-economic variables providedby the Office of National Statistics. One of the two districts that demandedthe largest real estate average price is found to be associated with fewerclaimants for council tax benefits, fewer houses in the highest council taxband, more people in good health, higher professional and skilled work force,fewer people living on state benefits, and more green space. The other areademanding the highest average price is identified with having few peopleliving on state benefits, and it comes only second to the best area in terms ofgreen space. The study also examines the causal relationships identified inthe case study for the entire city of Manchester. The average annual pricesof four property types within Manchester (flats, semi-detached, terraced, anddetached) are regressed against changes in employment, household income,inflation, and council tax. Flats’ prices are found to be the most sensitive tochanges in income, as flats are the most affordable for real estate buyers.