Data-Driven Analysis

Time For Homes believes in a methodical, rigorous approach. As such we have a Data for Good program that captures a variety of data from both government sources and other nonprofit organizations in order to drive change in an efficient, informed manner.

We seek to:

  • find the root causes of homelessness

  • model the problem

  • predict impacts and improvements due to programs and policies

  • improve the data available on homelessness and its causes

Time For Homes recognizes that it is exponentially more difficult to improve a program if we aren’t able to measure its success. We operate under a theory of change that allows us to constantly evaluate our operations, with perpetual feedback loops that are built-in at each stage and level of our projects.

This further allows us to be transparent, creating richer, longer-lasting connections between us and our stakeholders.

In addition to ensuring we are guided by accurate data and analysis at all times, we share our data science programming and resources with our partner organizations.

Data For Good

Our “Data for Good” program aims to use data to help inform policy making and program creation.

It is, in essence, applying the concept that the private sector uses to save money, to instead save lives—while remaining fiscally responsible.

To break down this far reaching goal into more manageable pieces we have three goals overarching in this program.

Gather Data

For our Data for Good program we want to gather as much data as possible. This does not simply refer to Point In Time counts or data sources directly relating to Homelessness, it also means that we want to gather data regarding things such as food stamp usage, Medicare/Medicaid usage, child welfare, and from various other related aspects to the Homelessness issue.

In order to accomplish the robust and widespread gathering of data we will collate data from the usual sources of first—Data sets from HUD, World Bank, and various other commonly used sources with similarly common data sets. The next level of Data will come from government agencies—some under Freedom of Information Act Law requests and others we’ll be able to do so with Memoranda of Understanding—and our partners.

This trove of useful data will enable us, and our partners, to more effectively engage with our collective programs, policies, and issues.

Analyze Data

At a basic level we will conduct data analysis to break down the raw data into understandable metrics and accurately answer questions like “where is the problem worst?” Or “who is affected most?” And variations thereof.

At a more advanced level we will attempt to create models and other applications to attempt to investigate more thoroughly the homeless issue. Some of these other applications are Neural Networks attempting to predict increases or decreases in Homeless numbers, or a data application that aggregates information on Homelessness into a word cloud to help break the issue into more manageable sub-issues such as substance-dependency, or access to work, or a Model which does the same thing as the Neural Network, but in a more concrete way.

These will be used to help inform policy and advocate change and progress. Time For Homes intends to share these applications and insights with our partners, fellows, and advisors—the issue is too big to solve on our own, everyone should have access to these tools and resources. As time goes on and the needs of our organization and those of our partners grow, we will add needed forecasting models and other applications, in order to serve all our stakeholders to the best of our ability.

Improve Data

One key aspect of our “Data for Good” program is the idea that we can improve quality of the data that is available regarding the homeless issue. Currently it is widely held that the majority of data regarding Homelessness is lackluster and not nearly as accurate as it should be. To be fair, it is workable, and that fact that it exists at all is commendable. However, there are ongoing efforts to create more accurate data on homelessness.

The methods most commonly used include but are not limited to gathering data from related sources, and creating new techniques to gather the data. We think, while this is all well and good, perhaps an analysis of the data itself could provide some level of improvement. We aim to use Statistical, Mathematical, and scientific techniques to use existing data to fill in its own holes. An example of one such technique is Matrix completion, and it is often used in the medical field to make tests and scans more accurate without sacrificing the ability to do these tests in the first place.

We hope to use similar or adapted techniques to look at the existing data and tune it to be more accurate through this more technical and advanced techniques.

By The Numbers: Equality?


of the Homeless Community are African American


of the Homeless Youth identify as LGBTQ+

Why African Americans, in particular, are disproportionately represented?

According to the national alliance to end homelessness, African Americans have been systematically denied equal rights and opportunities. The effects of long-standing discrimination linger and perpetuate disparities in poverty, housing, criminal justice, and health care, among other areas. These disparities, in turn, can contribute to more African Americans experiencing homelessness.

Clearly, it will take some systemic changes to build a truly just and fair society. Only with your help and the help of our partners/fellows/advisory board members can we achieve that.