Improved Margins and Higher Sales With Dynamic Real Estate Price Predictions

Use enhanced data to forecast price changes and boost home sale profit margins

The real estate market is dynamic and can change based on a variety of factors that range from external conditions to individual preferences. For realtors and investors, understanding how prices will change, as well as knowing when and where to make a purchase or sale is vital in earning profits.
The problem

Our customer had built an in-house rules-based model to price properties they were buying and selling but had recently found that it was undervaluing properties more often than not. The company was basing its pricing on standard factors that included a static neighborhood slide rule, square meters, and historic internal pricing data they already possessed. However, the model was not able to account for changing preferences in the area which could impact willingness to pay at certain prices, and which limited the realtor’s ability to sell properties at the optimal time and price point.

The solution

Beyond simply looking at historic prices and square meters, the real estate firm understood that they needed a pricing model that more accurately represented real-world demand and conditions. Historic price records don’t always account for shifting external conditions and events that could cause massive volatility in home pricing. Instead of a rules-based pricing model, the company was able to use Explorium to create a new pricing system that considered a variety of factors, including: 

  • The number of schools, parking, and post offices available in a certain radius 
  • The average closing hour of local businesses 
  • The local average food rating
  • Atmosphere ratings
The results:

Improved margins and higher sales

The company saw an immediate boost in its revenues and its ROI. While they had been closing sales before deploying the new model, their margins had hovered below 10% on average, largely due to sale prices that, while close to market value, didn’t truly maximize the potential of each property based on demand. After putting their new price prediction model into production, the company was able to quickly improve their margins to 14% and reported a higher sales volume in the quarter following implementation. On the whole, the company’s new model means that it can more accurately price homes, leading to fairer pricing without affecting their profit margins. With a higher sales volume overall, the company’s model lets it resist volatility impact by adjusting prices on the fly.

Use enriched data to stay ahead of the competition and predict changes in real estate.

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