HUD Multifamily Housing Assisted Properties

Detailed Overview of Subsidized Aid Programs and Population Characteristics

By Matthew Schnars [source]

About this dataset

> HUD’s Multifamily Housing property portfolio is integral in providing secure, affordable rental units for low-income households - including seniors, those with special needs, and disabled individuals. Our coverage includes apartments and townhouses but can also include nursing homes, hospitals, elderly housing centers, mobile home parks and retirement service centers. Through subsidies and grants to property owners and developers we further our mission of promoting the construction and preservation of low cost housing opportunities. > > This dataset comprises such properties from across the United States; located via our enterprise geocoding service which uses latitude/longitude coordinates as well as associated attributes for those addresses that can be geocoded to an interpolated point along a street segment or a ZIP+4 centroid location. Ultimately this dataset provides key insights into multifamily housing availability throughout the U.S., providing valuable information regarding individual buildings associated with each property while also giving us direct insight into population serviced by such programs in terms of their developmental needs or disabilities which may exist. With these metrics in hand we are better equipped to identify areas where reduced prices on rental units may benefit local communities the most - aiding populations that require our assistance the most directly! Data Dictionary: DD_Multifamily Properties; Date of Coverage: 12/2017; Data Updated: Quarterly

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How to use the dataset

> > First, let's talk about what kind of information you can get from this dataset. The HUD Multifamily Housing property portfolio consists primarily of rental housing properties with five or more dwelling units such as apartments or town houses. It includes subsidies and grants provided by HUD in order to promote the development and preservation of affordable rental units for low-income populations, and those with special needs such as the elderly, and disabled. > > The data contains geographic location information (latitude/longitude coordinates), city/state/ZIP code information as well as other internal identifiers for each property in the HUD portfolio. Additionally, there are columns related to different assistance programs which were previously noted above - Section 8 Project Based Assistance, Section 202 Supportive Housing for the Elderly and Section 811 Supportive Housing for Persons with Disabilities). > > In terms of how to use the data itself: due its size it is best explored by importing into an existing database solution like PostgreSQL or MongoDB where you can create custom views based on query keywords that bring back more meaningful results upon execution. Once you define what properties fit your criteria of interest then further analysis can commence - statistical trending on occupancy rates among much else given that next level drilling tends to open up many interesting possibilities like revenue forecasts creating new business models etc.. > And speaking of statistics one area worth noting here specifically is that location data provided by HUD may not always be 100% accurate so caution should be exercised when running analytics queries because incorrect locations will lead astray any conclusions reached after running said queries - so feel free to double check your results before leveraging them in any form whatsoever! > > Finally keep in mind that since there has only been one version of this dataset released thus far it would probably beneficial if you checked back every quarter or so just incase any changes were made particularity related to program eligibility requirements / reporting requirements etc.. > > Hope this helps and good luck on your journey considering using 'Hud Multifamily Properties Data' for whatever project at hand 😊

Research Ideas

> - To identify potential opportunities for developers looking to add affordable housing in underserved areas. Depending on different factors such as the income of the local population, geography, and the type of funding method used to support these projects, developers could be more likely to invest in those areas that have previously been assisted by HUD programs. > - To evaluate performance measures of existing affordable housing across different locations within a given region or state. This could help identify any discrepancies between properties or address any issues within a specified area or program by comparing various performance metrics including occupancy rates, rent collections, etc. > - To implement a recommendation system for local governments who are looking for assistance from HUD’s programs related to Multi-Family Properties Assisted by evaluating factors such as number of occupants and availability of services in each area or region which can result in better implementation of funds allocated towards these projects

Acknowledgements

> If you use this dataset in your research, please credit the original authors. > Data Source > >

License

> > > See the dataset description for more information.

Columns

Acknowledgements

> If you use this dataset in your research, please credit the original authors. > If you use this dataset in your research, please credit Matthew Schnars.