Real Estate meet Data Science. How ML work for house price prediction part-1

Data science and data analytics can be used in many ways in Real estate market. Main purpose of using data science in real estate is to collect datasets from multiple sources and extract useful information from them, Human are not able to analyze such Big data unlike algorithm.

Data analytics providing analysis on Properties for buyers and renters. many real estate company like Tralize, zillow and other real estate professional are getting more data on potential commercial/Real estate properties as well as feature about them that may motivate buyers.

For data analytics there is also other external data to analyze like how far properties from the market luxury retail store, what is distance of nearest park, crime rate in area and so many other details which useful for buyers.

Benefits of Data science in Real estate

  1. Reduces Risks : with the help of predictive analytics company can use it to estimate the overall condition like its ages, deconstruction history, owner information. company can provide their customer up-to-date information so is increase their satisfaction from working with them.
  2. It helps calculate the exact price : precise cost calculation in the real estate is time consuming, how the Machine learning algorithm can use for the estimate the price of properties with the help of historical data.
  3. Data driven decision : Machine learning open many opportunities for the business. just feed the algorithm with data and it will process it to help you make the right decision.
  4. Marketing strategy : with the help of customer information company can plan their future marketing strategy according to customer needs.

In the past data analytics in real estate is focused on pricing and market analysis. Price estimate and data-driven dynamic pricing algorithm are some notable example.

American online real estate database company zillow is used data science in real estate market. Zillow determines an estimate, also known as a “Zestimate” for a home based on a range of publicly available information, including sales of comparable houses in a neighborhood. According to Zillow, the Zestimate is a starting point in determining a home’s value.The accuracy of the Zestimate varies by location depending on how much information is publicly available, but Zillow allows users to check the accuracy of Zestimates in their own region against actual sales. In March 2011, Zillow released Rent Zestimates, which provide estimated rent prices for 90 million homes.

In 2007, The Wall Street Journal studied the accuracy of Zillow’s estimates and found that they “often are very good, frequently within a few percentage points of the actual price paid. But when Zillow is bad, it can be terrible.

In next part 2 we will see how house price estimate model build, what are the data prepossessing and feature engineering techniques use for it.

Part 2 :- https://medium.com/@jigar18011999/how-ml-work-for-house-price-prediction-part-2-aa828888f944

Data science | Data Analytics | Machine Learning | Deep learning