Real estate is hyperlocal.
That phrase has stuck with me since my new agent training nearly 6 years ago.
If you’ve never heard that, you’ve probably at least heard the phrase “location, location, location!” which is meant to convey the idea that value in real estate is held in the land more than in the improvements that sit upon the land.
If you take 3 identical houses and spread them across the city, you’ll find that they don’t hold the same value because it’s location that dictates value in many ways more than the attributes of an improvement.
This means that to estimate value in real estate you need to be able to identify neighborhoods. If you can’t accurately define a neighborhood, you can’t accurately estimate the value of a home using comparative analysis.
Where are we to start?
Zip codes are too big. They often seem to be an industry standard but I’m not sure why as they don’t seem useful to me at all.
Calculating the median home value or the percent owner occupancy in a zip code may look interesting but in reality, it’s pretty much useless because the areas are too big. Remember – real estate is hyperlocal. You’ve got to get small. How small? As small as you can while maintaining a large enough data set to analyze.
So zip codes are out.
What other options are there?
Many people depend upon their intuition or “market expertise” which they’ve built up through years of living and working in an area. The problem with this method is that it’s subjective and it limits you to only being able to analyze properties in areas you know something about.
The same goes for leaning on the intuition of others, which seems to be a popular replacement for remote investors.
They call a real estate agent who they assume will be able to tell them boundaries of neighborhoods. Here’s the thing I realized when I moved to OKC: these cities are large and ever changing. So, in my opinion, relying on the opinion of any so-called expert who can’t provide an objective process from which he derives his results is risky.
“But wait”, you may be saying, “what about subdivisions or school districts or any of the other meaningless arbitrary geographical boundaries that can be found around a city?”
Just because years ago, some developer decided to name this whole area the same thing doesn’t mean that it’s the same today, and it doesn’t mean that people looking to purchase today will consider one side of the neighborhood comparable to another side.
It especially doesn’t mean that a named subdivision or school district can provide a valid data set with which to analyze value.
What we need is a way to objectively map a city in a way that is statistically relevant and that mirrors the feelings of the actual residents of the place. Lucky for you and I, someone has already done that job for us. Here’s to you, US Census, and your implementation of census tracts.
So why census tracts? What first drew me to them, besides the above definition, is that they’re smaller than zip codes. They also don’t leave gaps in the city like so many “neighborhood maps” you can find online do.
But even better, the intentions of the people creating these tracts matches so closely with the intentions we would use to create such a map for real estate analysis that it almost brings a tear to my eye. Census tracts are literally defined for statistical purposes. The US Census and various American survey societies use census tracts and other census areas to try to lump people together into culturally and demographically significant and similar boundaries.
In fact, it turns out that census tracts are the smallest area for which there is property data in most countries!
Ultimately though, the biggest reason I’m comfortable finally speaking openly about using census tracts for identifying neighborhoods is because census tracts have vigorously passed the use test inside our company.
I’ve been testing the use of census tracts for defining areas for comparative analysis for a little over 2 years now. In my experience, using census tracts has made my analysis more accurate, more objective and more precise than any other method I’ve previously tried. It’s also significantly cut down on my analysis time by allowing me to systematically determine whether a data point is comparable or not.
Take the 73120 zip code, which our office is located in. If you didn’t know the neighborhood, you’d probably be able to guess that by looking at the zipcode alone that this entire area isn’t comparable.
But how do you figure out what boundaries to draw for comparison? This is something you could probably discover through the comps themselves more than likely or maybe even by walking the neighborhood or calling a real estate agent.
But what’s beautiful about using census tracts is that the US Census recognized it for you, and when you look at the above map you can see the separation immediately without calling someone or walking through or living nearby.
Like any unintended use of an application, using census tracts comes with its weaknesses as well as its strengths. The main weaknesses tend to appear in exceedingly small markets or areas that have experienced great amounts of change since the tracts were last updated in 2010.
My hometown of Ponca City, for instance, has only a few census tracts for the entire city, which can make them less useful for pulling comps.
In general though, I’ve found them to work exceedingly well in OKC and Tulsa, and I imagine that most midwestern markets of equal size would probably work as well.
In my career thus far, I’ve found that the ability to efficiently and accurately estimate the after-repair value of a property, the rental value of a property, and the desirability of an area all hinge on one’s ability to select a geographical area. And in my experience, census tracts are the answer for systemizing that skill.