This report was commissioned to determine if neighborhoods that contain Community Land Trust homes slow gentrification within the City of Chandler.
The Community Land Trust (CLT) model is used to create affordable housing, community gardens, civic buildings, commercial spaces or other community assets. The CLT model was created in the United States by Robert Swann in 1969 as a means for African American farmers to gain access to farmland and to be able to work the land securely. Structured as a nonprofit corporation, many CLTs throughout the US focus on affordable housing including Chandler’s Community Land Trust. By owning the land, the CLT is able to sell the house to an individual with low or moderate income at an affordable rate as the owner is only buying the house and not the land below. The land trusts often have a land lease to the homeowner for 99 years which is renewable. If the homeowner wants to sell, they agree to sell the home at a restricted price so that the house remains affordable to the future owner. In the United States, there are over 225 Community Land Trusts ensuring affordable homeownership for individuals. In Chandler, the Community Land Trust is run by a non-profit, Newtown Community Development Corporation. They have 68 homes in Chandler and use federal Neighborhood Stabilization Program funds and HOME Partnership Program funds to purchase new homes to continue expanding.
This report utilizes data from the Census Longitudinal Tabulated Database. This database encompasses data from 1970, 1980, 1990, 2000, and 2010 in both the sample and full datasets. In addition, the report utilizes Community Land Trust data encompassing the census tracts and information regarding the homes purchased through the Community Land Trust. To the best of our knowledge, the information in the databases are accurate and reliable as of the date of publication.
Utilizing the databases, the researcher maneuvered the data to obtain the necessary analysis. After the data was loaded and merged appropriately, the descriptive analysis of neighborhood change was performed. The researcher investigated the median home value change annd growth as well as the neighborhood metrics predicting gentrification including education level, poverty rate, family poverty rate, proportion of renters, and proportion of minorities. After the variables were created, the demographics and health metrics could be summarized and further displayed as well as the mapping of the variables.
After neighborhood change was presented, the defining variables of gentrification could be combined and then applied to the Community Land Trust data. The variables were used to determine how many Chandler tracts were eligible for gentrification, how many did not gentrify, how many that contained Community Land Trust homes did not gentrify, and how many that did not contain Community Land Trust homes did not gentrify. The descriptive statistics could be analyzed for each of the scenarios and determine the impact of the Community Land Trust homes.
Overall, the researcher showed that the Community Land Trust had some impact on gentrification. Although median home value and home growth was slower in tracts that did not gentrify that contained Community Land Trust homes, there showed to be more homeowners than tracts that did not gentrify that did not contain Community Land Trust homes. The variables of poverty, family poverty, education, and minority population stayed relatively the same across non-gentrified tracts. However, the percentage of renters doubled in non-gentrified tracts without Community Land Trust homes. Although further research is needed after future census data is available, the Community Land Trust program does show a slowing of gentrification within Chandler.
The code presented is a basis to determine which census tract variables define gentrification and can be changed or manipulated based on the definition of gentrification. The flexible code allows for a reproducible data process by changing the census tract variables. Using the data wrangling folder and the steps in analysis, variables can be changed and incorporated in a variety of ways therefore determining a different success of the program.