How real estate firms can make smarter investments by leveraging alt data

Risk and opportunity management is part and parcel of real estate investment strategies – discover new ways to create property development models that work in tandem with what is happening on the ground. 

This article will take a closer look at:

What drives property valuations?

Property value both in terms of the rental market as well as appreciation in the ‘for sale’ space is influenced by a wide range of factors, including:

  • Universities located in the property’s vicinity 
  • Shopping centers and retail diversity (shops, cafes, banks) 
  • Proximity to highways and freeways 
  • Crime rates 
  • School districts 
  • Public infrastructure 
  • Planning committees’ zoning decisions

The pains of investors utilizing traditional data sets 

Real estate professionals however would attest to the fact that it is indeed a curiosity at the least, and a mystery at best that properties located in the same zip code with the same:

  • school districts
  • crime rates
  • shopping centers 

Can vary considerably in terms of price valuation.

Traditional data sets rarely offer clear and quick answers to these questions. More often than not, traditional data sets such as:

  • New construction starts in a given area
  • The number of days an average property sits on the market
  • Job growth 

May be much less effective and indicative of the risk/reward analysis of a specific property and may only give very generalized insights regarding a certain neighborhood or city.

What do alternative data points have to say about real estate?

For investors who want to get more granular about the data they analyze in order to devise smarter market entry/exit strategies, alternative data sources have a lot to offer. For example:

  • Reviews of local businesses (restaurants, coffee shops, cinemas, theaters, and other cultural institutions) 
  • Energy independence when compared with other structures in the area (for example the existence or the lack thereof of solar panels) 
  • The number of permits issued for property extensions (additional floors, bedrooms, bathrooms, sunrooms) 

These data sets can help investors, building companies, and other interested parties really narrow down data insights at the ‘local’ and ‘city block’ level in order to make better-informed decisions about the trajectory of a specific property or land parcel.  

How asset managers are leveraging alt data in real estate 

Many asset managers and project developers are using Artificial Intelligence [AI] and Machine Learning [ML] to create predictive algorithms that stitch seemingly unrelated alt data sets together in order to produce concrete property analyses. 

A developer may want to analyze the Multiple Listing Service (MLS) in a specific area in order to identify areas with the most traction and potential. He or she may cross-reference this relatively ‘standard data set’ with alternative data such as the number of cafes and positive reviews of cultural venues in the area in order to identify a trend. Young people, including millennials and students, may be attracted to this area which also happens to be within a 2-mile radius of the local university. This specific area may have been overlooked by other investors if it were not for the correlations found between these traditional and alt data sets. This developer may very well not have been able to identify undervalued parcels of land zoned for development at an under-market price if it were located within a 1-mile radius of the university. But due to these additional alt data sets, he is able to quantify the added value of the area in question, allowing him to develop student housing in a place that may have previously seemed an unlikely choice. 

This same principle can be applied to other types of housing projects, for example, elderly/assisted living. Data points such as:

  • Number of walkers, wheelchairs, and hearing aids sold in a given area
  • Retirement activities advertised in a specific neighborhood (bingo, knitting etc) 

May be a good indication that you are looking at a population in your target age range. This correlated with data that indicates property downsizing (selling large homes in favor of smaller condos), for example, may mean this is the ideal place to build a retirement village. 

Alt data Insights for a buy-to-lease property portfolio strategy

As an investment group, private equity investor, or pension fund looking for a smart buy-to-lease opportunity, will probably want to build rent appreciation models before making financial commitments. They may want to combine traditional and nontraditional data sets in order to reach more accurate and nuanced conclusions. Traditional data sets that can be utilized in this instance are historic vacancy rates and rent fluctuations. Alt data that can be added into the mix may include:

  • Highly rated bars and restaurants in a 1.5-mile radius
  • Number of gas stations in a 3-mile radius 
  • Number of groceries and convenience stores within a 0,5-mile radius

These alternative data points often have a much larger bearing on rent valuation predictions as they represent the quality of living of future tenants and thus are more telling of future tenants’ willingness to pay a premium to live in an area.

Rounding it up 

Parties involved in this industry are constantly looking for a competitive edge, especially when looking to be ‘the first man on the ground’ of a neighborhood undergoing a gentrification process (Harlem in New York City is a great example). But traditional data sets and sheer ‘intuition’ lack the nuances that consumer-generated data can provide:

  • Where is the next artistic/musical revolution happening?
  • Where do students want to live?
  • What facilities are important for elderly residents? 
  • What will drive single-family home valuations in 5 years’ time?
  • Which land parcels have the highest project valuation potential?

No one has a definitive answer to these questions but analyzing and cross-referencing alt data together with classic real estate data sets can lead investors towards better decision-making and ultimately increased profitability.

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