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Does Living Near a University Boost Home Prices? Duke and Durham As a Case Study

By Mischa-von-Derek Aikman  Does Living Near a University Boost Home Prices?

The purpose of this paper is to explore the possible existence of a correlation between the proximity of one’s home to a higher education institution (such as Duke University), and the monetary value of that respective home. I have decided to use the residential structures surrounding Duke University’s East Campus as the sample population for this study. More specifically, I analyze and contrast the historical price trends for those homes located within one block of the perimeter of Duke’s East Campus, with homes located two blocks away from the same perimeter. The details of the specific geographic locations of these homes are discussed more thoroughly in the paper’s analysis. I propose that there is increased property value associated with living closer to the physical location of the University relative to living farther away. This difference in value appears to be apparent even in homes that are within one block of each other.


As mentioned above, the homes selected for the study were those located within a 2-block radius of Duke University’s East Campus. The annual historic prices of each of these residential homes between 2004 and 2014 were gathered using Zillow.com’s respective “Zestimate.” The “Zestimate” value is the median Zillow estimate of prices of all the houses in a given geographic location. As of May 2010, the index had tracked over 200 metropolitan areas, and had successfully calculated the index for 120 of these locations. Therefore, the extensive nature of the index made it suitable for the purposes of this study. The homes were divided into two groups; one for those located within a one-block radius of East Campus, and the other for those located within the second block of the East Campus perimeter. The reasoning behind this division was to determine if there is a significant price gap on average between homes located more closely to the University relative to those that are farther away. The specific monetary values for each home can be found in the appendix. The Zestimate at the Durham County level was also collected for this time period to be used as a benchmark. It is important to note that commercial buildings and apartment complexes within the given radius were excluded in an attempt to control for the types of residential structures being observed. The table and annotated map below show which streets the two groups spanned.

Table Showing the Streets each Respective Group Spanned 

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Figure 1: Annotated Map Used for Study

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The homes that fall between the green and blue borders constitute those placed in Group 1 (located closer to Duke), while those that fall between the blue and red borders constitute Group 2 (located farther from Duke). Pricing information was gathered for a total of 485 homes over the 10 years.

Observed Trends and Analysis

The mean home prices for each year was then calculated for both respective groups, and were then plotted against each other along with the Durham County level Zestimate.

Figure 2: Mean Home Prices for Group 1 and Group 2

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It is obvious that there is a significant and consistent price gap between those homes located within one block of Duke’s campus, and those situated two blocks away. It is also very interesting to notice that despite the price gap, both housing groups seem to have been appreciating at more or less the same rate over the past decade. Plotting the price gap itself, as we do in Figure 3 on the next page, shows an unequivocal spike in the price gap between 2007 and 2008. This can be attributed to the culmination, and subsequent burst of the national housing bubble in 2009. Trulia’s chief economist asserted that “geographical home prices was widest in 2007, the peak of the housing bubble.” This may have translated into the unusual widening of the gap at that particular point in time within this subsector of the wider housing market.

Figure 3: Price Gap Between Groups 1 and 2 

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Regarding the significant price gap between the two defined housing groups, we can look to the apparent economic implications of being located within close proximity of a prestigious University. Although extensive literature does not exist on these effects, it is common knowledge that Universities impact communities socially, culturally and economically.


First, is the factor of employment. As of 2006, Duke University was the second largest private employer in the state. A significant portion of the population living within immediate proximity of the University (i.e. within the one block radius) will tend to be well-paid members of faculty and staff within some facet of the University such as health care workers and professors. Hence, these residents are likely to be more financially stable, and equipped to pay more rent than their average counterpart. While it is also probable that some percentage of these employees also live within the two block radius of campus as well, the desired real estate will be that which is more convenient, and therefore, closer to one’s place of employment. This augmented demand within a targeted group of individuals may contribute to the higher prices found within this geographic region.

Investment Incentive

Second, is the very attractive opportunity to invest in real estate near Universities. Zillow’s chief economist, Stan Humphries, asserts that “a lot of students will live off campus, there’s built-in rental demand.”4 The very high flow of students and faculty from year to year lowers the risk investors run by renting homes to tenants near Duke, or any university for that matter. Vacancy rates will be much lower relative to other areas given the continuous demand for housing. Therefore, the heightened demand from the faculty and staff’s perspective, feeds the investor’s growth of demand, who are more confident in the long-term returns on their investment, as well as the short-term security of it. This might be another reason those houses in Group 1 were consistently valued higher than those in Group 2.

Location, Location, Location…

Just as acquiring a beachfront property will typically cost more than the average home, it can be argued that the same is true with purchasing a home close to a University. Housing is an asset that, despite crashes like that of 2008, ultimately appreciates over time. Being located near Duke University, one of three major points in the Research Triangle, intrinsically implies that one is located in an “established” neighborhood. It is very unlikely for the ‘status’ of this neighborhood to decline over time, as the physical University is essentially an immovable asset. This point is further supported if we take a closer look at Figure 2, which plots the Annual Mean House Price for both groups. Although the housing bubble burst in 2008, Durham real estate prices in both groups did not experience a dip in prices until late 2012, leading into 2013. This three-year lag in the reaction of housing prices suggests that residencies located near a powerhouse University such as Duke may be privy to some level of insulation from national market occurrences. This supports the paper’s rationale even further as to why properties in Group 1 would be more desirable, and therefore, more expensive compared to those in Group 2.

Isolating the Outliers

Another interesting observation was that while the house values were cheaper on average in Group 2 than they were in Group 1, there were a few outliers. More specifically, there were occasional strips of Group 1 homes that were far cheaper than quite a number of Group 2 homes. In order to isolate the streets along which these outliers existed, the historical mean price of homes were calculated for each street within each individual group (see appendix for Group 2 data).

Figure 4: Iredell as Outlier for Group 1 Housing 

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For the streets covered within Group 1, the majority of the mean historical prices were range bound between approximately $175,000 and $250,000. The means for Broad St., Minerva St. and Watts St. were all higher relative to the others with a maximum mean of $531,529. The obvious outliers in this case were those houses located along Iredell St, whose historical means floated very consistently around $75,000 throughout the entire decade. Why are these houses valued so much lower than the others found in its group? If one were to look closely at the annotated map in Figure 1, he will notice that Iredell Street was on the furthest most point of the boundary used to confine houses in Group 1. It is possible that the ‘one-block vs. two-block’ measure might have been too neat of a divide, and that the very outskirts of Group 1’s boundary had already transitioned into homes which fit the characteristics of Group 2 more appropriately. However, this outlier still did not cause for the hypothesis to be rejected.


While the results of this study were informative, and supported the original hypothesis, there were a few facets of the experiment that may have limited the level of conclusiveness. First, is the use of the Zillow estimate to gather the historical prices for the homes. It was a suitable index to use within the scope of this experiment since it uses public data on house attributes and actual sales prices to develop its model. However, the academic community often criticizes it for its lack of publically available historical time series.

Second, it is clear that the sample size used in the experiment is relatively small. Having surveyed 4,850 historical prices for 485 homes in Durham provides a nice picture for the community immediately surrounding Duke University. However, there would be great value in expanding the boundaries throughout a larger geographical spread within Durham.

The final limitation is concerned with the method used to define the boundaries that divided the residential homes into two groups. As was seen in the “Isolating the Outliers” portion, the evidence suggests that the border may have been too rigid of a split. Perhaps one could observe more accurate price correlations using the metric distances from the center point of the university and each respective home. This allows the distance factor of the model to be continuous, not discrete, and can speak to even more meaningful relationships.

Summary and Conclusions

Using Duke University and Durham as a case study, we were able to observe significant relationships between historical housing prices for homes located closer to the campus (Group1) relative to those located farther away (Group 2). We noticed that while the houses in both these groups appreciated at rates that were relatively very similar, there was a consistent price gap between houses located within one block of Duke’s campus, and those located two blocks away. More specifically, the homes within the first block were consistently more expensive than those in the second block by significant amounts. Various reasons that could potentially contribute to the existence of this gap were discussed. These included the impact that Duke University has on the employment of those who live near campus, the attributes of homes situated near a university that attract investors, and what seems to be some kind of cushion against larger market phenomena such as the housing crash in 2008. All of these supported the hypothesis that homes located closer the Duke’s East campus, were consistently more expensive than those located farther away over the past 10 years.

Moving forward, it would be very interesting to conduct the same study on a larger scale for numerous universities across the United States. The differences in results between private Universities and State Schools, or between Universities whose campuses are compactly designed (such as Duke University) vs. those that are dispersed throughout a city (such as North Carolina State University) would prove to be very useful within this topic.

Work Cited:

  1. Dougherty, Conor. “Gap Between Most, Least Expensive Housing Market Still Wide.”Real Time Economics RSS. The Wall Street Journal, n.d. Web. 27 Mar. 2014.
  2. Duke and Durham:AnAnalysisofDukeUniversity’sEstimatedTotalAnnualEconomicImpactontheCityandCountyof Durham. Rep. Durham: Office of Public Affairs, 2006-2007. Print.
  3. The Identification and Estimation of A University’s Economic Impacts.G.GeoffreyBoothandJeffreyE.Jarrett.The Journal of Higher Education. Vol. 47, No. 5, pp.565-576
  4. TrackingtheHousingBubbleAcrossMetropolitanAreas–ASpatio-TemporalComparisonofHousePriceIndices.Laurie Schintler and Emilia Istrate. Cityscape. Vol. 13, No. 1, Discovering Homelessness (2011), pp. 165-182)
  5. Woolley, Suzanne. “Real Estate: Investing in College Towns: A Degree in Real Estate”. Bloomberg.com. Bloomberg, 5 Nov. 2012. Web. 20 Mar. 2014.

Appendix: [you may find all the data in Appendix here Does Living Near a University Boost Home Prices? ] 

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  1. Hi,

    I thought your topic was a great choice and a very interesting one at that. As students the home values and rental prices definitely affect living options and thus I find your paper of particular interest.

    I think it’s great that you were able to gather so many data points through zillow – from my understanding that involves a great deal of manual input.

    One recommendation I have is that instead of demarcating section one and section two in terms of the number of blocks from the school, rather draw it based on each individual home’s distance from Duke University. I think that that would result in more accurate results as well as explain the results of the data analysis.

    Another comment I have on the delineations for the housing sections analyzed is that the one-block two-block marks seems somewhat arbitrary. Perhaps basing these distances on previously literature on the trends of housing price shifts from major centers would make these marks more meaningful.

  2. Mischa,
    I thought this was a well-developed study, and you outlined the paper in a very accessible way, so that I could follow exactly how you approached the issue. The structure of the paper made it very easy to go from the identifying the issue to data analysis to interpretation of the results. You also offer some good interpretation of the data you collected, which supports the significance of your findings.

    I agree with Cecilia’s suggestion of making the dependent variable of distance a graduated measurement, I think that will give you a more holistic view of the relationship between property value and distance from Duke. Another point of consideration is observing the trends in property transaction prices as opposed to Zestimates, which are a little more opaque in terms of what it reflects in a property. Transaction prices are also listed in Zillow and date back several years, which might make for a solid set of data.

    Overall, I think there’s a lot to work with here and the subject is certainly one that bears exploring.

  3. Mischa,

    Of the papers on our class’s website, yours is one of the most clearly structured and well-organized. I appreciated your bold headings for each section, appropriately labeled figures, and concise grammar and word choice. Your writing is very directional and easy to follow. I also like that in your summary and conclusions section, you did not attempt to make any sweeping comments about causation or more general trends. You simply state that houses within a block of Duke University’s East Campus are more valuable on average than those two blocks away, making the additional point that this phenomenon could be due to various factors.

    Offering some constructive criticism, you might find it useful to mention more information regarding existing literature in the field of urban economics that deals with housing values. Others have already mentioned using metric distances rather than blocks in comparing house values and I agree with their sentiments. If you have time, you also might consider performing a side-by-side analysis between Duke’s East Campus and NC Central’s campus. This could provide more evidence to support your conclusions. Overall, good job!

    Matt Lee

  4. Mischa,

    I like this topic and think your findings are interesting, though not surprising. I’d love to expand the sample size to not include Duke’s East Campus/Durham, but 4-year universities across the country. The analysis could also include middle schools, elementary schools, and high schools, though literature probably exists regarding these factors.

    I think the Duke sample is too small to conclude any significant findings for a couple of reasons. First, the sample size is very small. But more importantly, the area around east campus is probably considered the same neighborhood, or maybe includes 2 or 3 neighborhoods, but not enough to make generalizations about whether universities cause housing prices to rise. Any trends in this sample could simply be confounded by the neighborhood-specific trends instead of the effect of Duke.

    I think another method to expand upon your analysis would be to create a regression instead of a comparison in means. In this analysis, we are only able to control for location. If we performed a regression analysis, we could control for general housing characteristics like total bedrooms, bathrooms, square footage, lot size, age, and a categorical variable for the neighborhood it is in. This could also help you control for your “distance limitation” that you addressed in your limitation section. You could incorporate a “walking distance from Duke campus border” which would help control for proximity to the university more accurately.

    Billy Marsden

  5. Mischa,

    I thought this was a well-structured paper that thoroughly supported your claim that living closer to East Campus increases the property value. I liked how you included the type of people that will be living in these homes closest to the campus because I believe this plays an important roll in maintaining these property values.

    I think it would be interesting to assess the crime rates in the two groups that you created. I wonder if there is a significant difference in crime one extra block away from East Campus. I believe this could be important for this topic because crime has a significant impact on property values. This extra analysis could help support the importance of living closer to a University. Additionally, I believe it could be interesting to conduct a similar analysis to a state school. I wonder if you would find the same results for a state school that has similar characteristics to Durham.

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