Author: Neha Asrani
We often hear that real estate investment is all about location, location, location. But how does a real estate investor choose that location? How do we use data to help him choose the right property for investment? How can we estimate the growth potential? This perspective primarily focuses on understanding and analyzing single-family homes in the US real estate market.
Successful real estate investors always say that money is made when a property is purchased, not when it’s sold. To generate cash flows in any real estate marketplace, knowledge and understanding of the target market plays a highly significant role. It helps buyers understand the characteristics of markets and aids them in choosing the right property to meet their financial goals. Decisions driven without the benefit of a detailed market analysis run the risk of not meeting the target rate of return.
Understanding data from sources
While performing a real estate market analysis, there are multiple ways to gather information in order to transform data into valuable insights. Listed below are a few parameters to be taken into consideration that affect the overall value of the property. These, in turn, determine its growth potential. Also listed are a few data sources and the data points they provide corresponding to those parameters.
Property Details – Attom Data Solutions (https://www.attomdata.com/) is a multi-sourced national property database that aggregates property tax, deed, and mortgage records. They provide data points about property characteristics (beds, bath, sq.ft., pool, garage, etc.), ownership, legal description, site address, historical sales, property tax, and assessed value.
Rent and Property value – Zillow (https://www.zillow.com/– an online real estate database firm) provides an API to pull the latest rent and value estimates. Zillow Home Value Index (ZHVI) is an adjusted measure of the typical home value and market changes across a given region and housing type. Zillow Rent Index (ZRI) is its measure of the typical estimated market rate rent. With this data, rental trends can be understood and appreciation can be evaluated.
Crime Score – This data is provided by SpotCrime (https://spotcrime.com/) – a historical crime database. It provides nationwide crime information about arrests, arsons, assaults, burglaries, robberies, shootings, and thefts. Crime score offers a weighted aggregation of different crime type incidence levels, communicating the local crime level relevant to the real estate investment context, etc.
Census Data – ACS (American Community Survey – https://www.census.gov/programs-surveys/acs) provides data points about population, education levels, and occupancy. The geo granularity is at Census tracts, CBSA (Core Based Statistical Area) and Zipcode levels.
Economic Data – John Burns real estate consulting (https://www.realestateconsulting.com/) is an independent research and consulting service related to the US housing industry. It provides data such as payroll employment growth, household growth, median single-family home rent and median household income to facilitate US housing analysis. U.S. Bureau of Labor Statistics (BLS – https://www.bls.gov/) also provides data about employment and labor by conducting a Current Employment Statistics (CES) survey released monthly.
Building Permits – Building Permits Survey (https://www.census.gov/construction/bps/) provides data on the number of new housing units authorized by building permits. Data is available monthly and annually at the national, state, selected metropolitan area, county and place levels. New building permits provide signals of growth.
School Score – Great Schools (https://www.greatschools.org/) provides scores for elementary, medium and high schools. On combining with the data from School Attendance Boundary Survey (SABS) shapefiles that provide polygons separating regions, these scores can add great value. Using Geo-spatial search for all Single-family homes in the US, assigned schools are identified by looking at the geo-point of the property that fits within the school polygon and ranked based on school score and distance.
Determining the Neighborhood Score:
If a two-bedroom property in Atlanta is sold at a similar price to that of a home at Texas, provided that both homes check a lot of right boxes, how do you choose which one could fetch better return over years? Neighborhood score helps here!
This rating is evaluated using a powerful algorithm that normalizes neighborhood data across markets and enables comparison of homes on an even footing. The dynamic system uses a statistical procedure called Principal Component Analysis (PCA) to measure a dozen key attributes using the data collected from the sources mentioned above. They include school district quality, home values, employment rates, income levels, crime scores and other vital investment criteria.
The raw data is initially cleaned followed by feature normalization. In order to facilitate dimensionality reduction, PCA is used. This technique uses orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components (PC). Each PC is a linear combination of all input variables and could be seen as a weighted sum of all variables after input variables are catered to and scaled. PCA-based dimensionality reduction tends to minimize information loss.
Following this, grades are assigned from 1-5 (1 being the least) at the census tract level. These values are mapped to individual properties using TIGER (Topologically Integrated Geographic Encoding and Referencing) Shapefiles (https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html) that define legal boundaries in order to identify the score of a property.
Generally, buyers perceive this score as one of the main governing sources that will determine the ROI.
Deciding the “Buy Box”
After a thorough understanding of the market through these parameters, investors can now fix their buy box criteria. It is a set of attributes (purchase criteria) that matter the most to them, depending upon their investment strategy. This is the phase in which investors decide the return they wish to reap, in addition to the time frame of holding and estimated ROI. The metrics that quantify the ROI include Cash Flow, Cash on Cash, Net Operating Income, Cap Rate, Gross Yield, Appreciation, and Annualized Returns.
The fact is that money is to be made in any real estate market, if invested in the right way. Performing a thorough real estate market analysis helps understand where you stand in the real estate market cycle and if it’s better to buy, sell or hold at that particular point of time. Buyers make use of market analysis to discover homes that fetch profits and that which seems appropriate to their investment strategy. On the other hand, sellers draw a real estate market analysis to decide on the ideal sale price and marketing strategies.
Real estate investment is a marathon, not a sprint. And clearly information and data empower investors to make an informed decision with confidence and satisfaction.Tags: AI for Real Estate