Project Overview
The Problem We are Trying to Solve
William Douglas Property Management would like to increase their customer base and overall improve their business. They are strong believers in their product but are looking for a digital advantage.
How We Aim To Help
By identifying neighborhoods with similar characteristics to current clients. Through analytics and an interactive dashboard, WDM is able to upload their client list (which is not saved anywhere) and find similar neighborhoods based on selected criteria
Dashboard Design
The dashboard was designed to allow for easy exploration of the individual metrics as well as simple identification of similar neighborhoods.
Analytically, the dashboard takes the selected metrics and normalizes them (scaling to between 0 and 1 to ensure uniformity) and calculates the distance between all neighborhoods. It then presents the user with the list of most similar areas
Special care was given to protecting client data. It is not saved anywhere on the server, it can be cleared within the session, and will reset with a refresh button or closing the window.
Analytically, the dashboard takes the selected metrics and normalizes them (scaling to between 0 and 1 to ensure uniformity) and calculates the distance between all neighborhoods. It then presents the user with the list of most similar areas
Special care was given to protecting client data. It is not saved anywhere on the server, it can be cleared within the session, and will reset with a refresh button or closing the window.
About the Data
The dataset profiles 462 neighborhoods in Mecklenberg County includes 26 seperate metrics across multiple years.
The Dataset was created in partnership among the City of Charlotte, Mecklenburg County, the UNC Charlotte Urban Institute, with the towns of Cornelius, Davidson, Huntersville, Matthews, Mint Hill, and Pineville.
The Dataset was created in partnership among the City of Charlotte, Mecklenburg County, the UNC Charlotte Urban Institute, with the towns of Cornelius, Davidson, Huntersville, Matthews, Mint Hill, and Pineville.
- Housing units per acre: Number of housing units, divided by total land area.
- Number of housing units: Total number of housing units
- Housing Units per Acre: Number of housing units, divided by total land area.
- Percentage of housing units that are single-family: Number of single-family units, divided by total housing units.
- Number of single-family housing units: Number of single-family units. Single-family housing units includes detached homes as well as duplexes, triplexes, and mobile homes. Duplexes and triplexes are counted as 2 and 3 units, respectively.
- Average heated area of single family housing units (Square feet): Sum of heated square footage of all single-family housing units, divided by the number of single-family housing units.
- Average age of single-family housing units (Years): Age of each single-family housing structure (calculated by subtracting the year structure built from year of data collection) is summed and divided by the total number of single-family housing structures. Single-family includes single-family detached units, duplexes, triplexes, and mobile homes, though each structure is counted only once regardless of the number of units within it.
- Percentage of detached single-family housing units that are rented: Number of single-family (detached) that are rented divided by the total number of single-family (detached) units.
- Number of detached single-family housing units that are rented: The number of single-family (detached) rental houses. Homes are considered rentals when the mailing address for county property tax documents is different than address of the property.
- Number of residential units permitted for new construction, per 100 acres: Number of residential units permitted for new construction, divided by land area, times 100.
- Number of residential units permitted for new construction: Number of residential units permitted for new construction
- Number of residential units permitted for new construction, per 100 acres: Number of residential units permitted for new construction, divided by land area, times 100.
- Residential units permitted for renovation per 100 acres: Number of residential units permitted for renovation, divided by land area, times 100.
- Number of residential units permitted for renovation: Number of residenital units permitted for renovation
- Residential units permitted for demolition per 100 single-family units: Number of residential units permitted for demolition, divided by single-family units, times 100.
- Number of residential units permitted for demolition: Number of residential units permitted for demolition
- Percentage of housing units in forclosure: Number of single-family, condominium and townhome foreclosures, divided by the number of single-family dwellings, condominiums and townhomes.
- Number of foreclosures: Number of single-family, condominium and townhome foreclosures.
- Housing code violations per 100 housing units: Number of housing related code violations divided by number of residential units, times 100
- Number of housing code violations: Number of housing related code violations
- Housing code violations per 100 housing units: Number of housing related code violations divided by number of residential units, times 100
- Percent of housing units twith development-based rental assistance: Number of housing units with development based rental assistance.
- Percentage of housing units with development-based rental assistance: Number of housing units with development based rental assistance. Development-based rental assistance includes proprerties with Low-Income Housing Tax Credits, developments from the Charlotte Housing Authority, Charlotte-Mecklenburg Housing Partnership, those fuded by government agencies, and those with Section 8 and HOME Rental Assistance subsidies.
- Number of housing units with development-based rental assistance: Number of owner-occupied housing units, divided by the total number of occupied housing units
- Margin of Error (Percent): The U.S. Census Bureau calculates standard errors for each estimate produced and publishes the margin of error above and below the estimate at a 90 percent confidence level (the Census Bureau standard). There is a 90 percent chance that the true value falls within this range: the etstimate plus or minus the margin of error.
- Percentage of housing units that are occupied: Number of occupied housing units, divided by the total number of housing units.
- Median gross rent of renter-occupied housing units (Dollars): Median gross rent as estimated by the American Community Survey. Median gross rent is inflation-adjusted to the most recent year of the five-year estimate. When an NPA is comprised of more than one block group, the median gross rent is calculated by linear interpolation for a range of rents published in the ACS.
- Margin of Error (Dollars): The U.S. Census Bureau calculates standard errors for each estimate produced and publishes the margin of error above and below the estimate at a 90 percent confidence level (the Census Bureau standard). There is a 90 percent chance that the true value falls within this range: the etstimate plus or minus the margin of error. The margin of error is not calculated for NPA's that are comprised of more than one block group. When combining NPA's, this variable should be used with caution.
Demographic Data
Metric
Neighborhood Analytics
Analytics Criteria
Neighborhood Selection
Similar Districts
Summary Results
Summary
While we were not provided the list of clients to perform the analytics directly, we were able to develop a tool that is capable of allowing the user to perform the needed analytics without any background in statistics or data analysis.
By providing a live dashboard with the ability to upload a clients list, the user is capable of diving into the metrics that are most important based on the HOA and the surrounding area. They also have the ability to identify new target areas that are not immediately geographically close. This can greatly reduce the area in which to perform outreach to potential new HOA’s.
By providing a live dashboard with the ability to upload a clients list, the user is capable of diving into the metrics that are most important based on the HOA and the surrounding area. They also have the ability to identify new target areas that are not immediately geographically close. This can greatly reduce the area in which to perform outreach to potential new HOA’s.