(This advanced blog summarizes real estate investing tips and insights Lofty AI has acquired from working with thousands of investors and institutional funds.)
Real estate investing should be data driven
The year is 2020. Every industry, including finance, insurance and marketing, are utilizing artificial intelligence to make better informed, data-driven decisions.
Every industry—except real estate, that is.
The lack of data science in real estate is truly appalling. Ask any real estate investor with a software background what their investing process is like, and I'll guarantee they've written some sort of basic script to help narrow down the list of the 10,000 currently available properties to a shorter list of more desirable properties.
Even a basic script is 10X better than most real estate investing websites out there.
Because of the clear absence of a data-driven approach, real estate investors who do harness machine learning in real estate gain a colossal advantage over their competition, while achieving more appreciation and higher returns than they ever thought possible.
In this post, we're going to focus exclusively on how A.I. can be used to predict property appreciation.
Predicting appreciation: The old way
Census data and quarterly reports with lagging data including projected population increases in an area, job growth, projected rent growth, and other non-granular city-level indicators.
Why the old way of doing things doesn't work
The data described above is updated very infrequently.
It's macro-level and not granular. Knowing that properties in Los Angeles as a whole are going to appreciate in the next year is irrelevant when there are over 50,000 properties for sale.
Everyone can gain access to the same reports at the same time, leaving zero room for a competitive edge.
Predicting appreciation: The new way
Real-time, alternative data with block-level granularity
Artificial intelligence to find patterns in the types of data that predict appreciation
Everything is data-driven, no more guessing
Why the new way of doing things works
The data is updated every single day.
It's granular. This data allows you to know, down to the street corner, which areas will see the most future growth and appreciation.
It is very difficult to gather this data, so if you're using it you gain a massive competitive advantage over every other investor.
We use over 50 leading indicators to predict property appreciation at the block level.
Below are a few examples of indicators we currently use.
Rising income levels
To determine rising income levels in a neighborhood, we track the wait-times for luxury ride-sharing services. If the wait times for these luxury ride-sharing services have been decreasing in an area over, say, the past six months, it is a clear indication that wealthier individuals are moving into that neighborhood—a very good sign for future neighborhood growth.
Rising education levels
We're able to identify, in real-time, if there is an increase of better-educated and tech-forward millennials moving into a neighborhood. We do this by determining whether there is an increase in the number of properties using high-speed internet in said neighborhood.
We pair this with data from job boards, which could reveal an increase in job postings for software engineers or social media managers—two high-paying jobs sought after by tech-forward millennials who are known to catalyze neighborhood growth and appreciation.
Airbnb data provides a number of useful insights. For example, if a neighborhood’s nightly rates have risen by 20 percent in the last six months and the neighborhood itself also sees a 15 percent increase in five-star reviews, this tells you that visitors are willing to spend more money on an Airbnb in that specific neighborhood and generally enjoy the experience.
We saw this trend occurring in Compton, Los Angeles over a year ago and wanted to see the effect it had on property prices. The rent of the few properties that we monitored, which are all within a granular radius to these Airbnb properties, went up 18% in just the next six months.
Dog breed populations
We’re even tracking increases in social media posts of certain dog breeds tagged in an area to identify wealth migration patterns in real-time.
For example, French bulldogs cost $3,000 on average; if you have been seeing more and more photos of French bulldogs tagged to a certain neighborhood over the past 3 months, it most likely indicates that wealthier people are moving into the neighborhood and signals prosperous future growth.
At Lofty AI, our goal is to give you—the investor—a massive competitive advantage by informing you of micro-neighborhoods primed for rapid appreciation six months before the rest of the industry has a clue.
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