AI invasion in real estate


In an increasingly virtual world, real estate may seem like the only real thing left, but it’s also being supported by artificial intelligence. AI is already being used by some of the biggest names in the industry, like Compass, Zillow, and LoanSnap, to help buyers find the right mortgage and home. This could already be a game-changer for real estate brokers. The majority of real estate information is open to the public, including property records, title deeds, purchase prices, and even mortgage liens. The problem was that going to the local offices and getting all the information was a tedious operation, which is no longer the case.

A computer system can now sift through millions of files in seconds for information on real estate appraisals, debt levels, home changes, and even some personal information about a landlord. New Zestimate method integrates a broader history of real estate data, such as sales transactions, tax assessments and public records, in addition to house characteristics such as square footage and location, using neural networks, l latest machine learning approach.

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What is the Zestimate algorithm?

The Zillow estimate, or Zestimate, is one of several real estate tools that use an algorithm to calculate an automated appraisal of a property. The Zestimate algorithm is calculated using unique valuation models based on information about property, tax assessments, and past and current transactions. Public data from county and tax assessor records, brokerage houses, and home details updated by owner users are among the data sources. A Zestimate differs from a comparative market research of a real estate agent. Zestimate is often calculated based on all the data for a county, whereas a conventional real estate agent determines the value of a home based only on data within the home area.

How it works?

Zillow recently upgraded the Zestimate algorithm to incorporate new advancements and even more data. The neural network model used in the Zestimate algorithm correlates facts, location, housing market trends and house values. The Zestimate algorithm blends data from county and tax assessor records, as well as direct feeds from hundreds of multiple listing and brokerage services, into a complex neural network-based model. Real estate data submitted by landlords, real estate experts or public sources will be compared to recently sold comparable homes nearby using the Zillow algorithm. Algorithm data points will include the following:

  • Site
  • Property area
  • The number of bedrooms and bathrooms in the house
  • The age of the property
  • Lot size

It then uses this data to calculate a likely selling price.

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Precision

According to report, Zestimate for on-market properties has a nationwide median error rate of 1.9 percent, while Zestimate for off-market homes has a median error rate of 6.9 percent. percent. The accuracy of Zestimate is determined by the amount of data available in a given area. More complete information about the home, such as square footage and number of bedrooms or bathrooms, is available in some places but not in others. The more data there is, the more accurate the Zestimate value becomes.

Conclusion

The Zestimate algorithm’s neural network model correlates facts, location, housing market trends and home values. In addition, moving to a neural network-based model will speed up Zestimate treatment. This means that the Zest is more sensitive to market movements and seasonality, which can impact the market value of a home, despite the fact that many real estate applications use an algorithm to create an automatic appraisal of a property.


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Dr Nivash Jeevanandam

Nivash holds a doctorate in information technology. He worked as a research associate in a university and as a development engineer in the computer industry. He is passionate about data science and machine learning.


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