Electronic Thesis and Dissertation Repository

Thesis Format



Master of Arts


Geography and Environment


Arku, Godwin

2nd Supervisor

Lee, Jinhyung



This thesis explored changing patterns in neighbourhood value for 20 US cities from 2004 to 2015. Neighbourhood Value was defined and measured as a combination of distance from key locations within the city and local conditions within the neighbourhood, as reflected in housing prices. Hedonic modeling was used to determine neighbourhood value from a database of four million housing transactions, which included for each house the sale date, purchase price, geolocation, and structural characteristics. Spatial interpolation was used to visualize neighbourhood value in each city over 12 years. Moran’s I was used to analyze polarization in neighbourhood value. The findings suggest that for some cities, distance factors drove neighbourhood value while in other cities, local attributes drove neighbourhood value. Rather than applying generalized rules, each city must be considered individually to understand how neighbourhood value works within it. Which factors drive neighbourhood value likely impact the effectiveness of municipal policy.

Summary for Lay Audience

This thesis looks at 20 US cities between 2004 and 2015 in order to examine neighbourhood value. Neighbourhood value is defined and measured by examining housing purchases and determining how much of the value of that house comes from where that house is located, such as being near a good school, rather than what the house is, such as it having three bedrooms. This study looks at the distribution of neighbourhood value throughout the 20 cities and how that neighbourhood value changes over the 12 years studied. It also examines if the areas of highest and lowest neighbourhood value are clustered together or dispersed throughout the city, and how that changes over the 12 years.

In this research, two major components of the house’s location are examined. The first is how far it is away from key locations in the city, such as downtown or the largest university. The second is the local conditions of the neighbourhood, such as crime rate and how good the school is that teaches students in that neighbourhood. This study looks at which of these two components is most closely related to total neighbourhood value throughout the city. It also looks at how these two components make neighbourhood value cluster or disperse over time.

The results show that out of the 20 cities, most of the cities had their neighbourhood value closely related to either just distance from key locations, just local neighbourhood conditions, or a strong mixture of both. However, there was no regional pattern as to which cities found which component (distance or local neighbourhood) to be driving neighbourhood value. Instead, each city needs to be considered individually. The component that drives neighbourhood value is important when considering city policy. A distance driven city would likely respond better to city-wide infrastructure plans while a local neighbourhood conditions driven city would likely respond better to targeted neighbourhood improvement programs.

Available for download on Thursday, August 07, 2025