Data science in Real Estate

Data science is a subject that combines mathematics and science to be able to use as a guide to decision making and strategic planning. Data science works hand in hand with Artificial Intelligence (AI), and has various advantages for the real estate industry. These days many AI techniques are being introduced and implemented by real estate companies in order to stay ahead of the competition in the market. Correct application of data science will help real estate companies yield lucrative revenue, make informed decisions and help clients in making proper decisions when it comes to investing in property.

Data science can be applied in many areas of real estate, that is, from identifying consumer interest to making informed decisions about property using geographic information systems. Let us understand a few ways on how data science is applied in real estate.

Firstly, the concept of investing and price indices. Property price indices can help analyze price insights and forecast trends from big information sets. For example, statistical methods can help provide insights into historical market performances. Here, investors can make wise decisions based on high-quality data and insights brought forward by the data science process allowing them to make more profitable decisions. Automated valuation models help us understand better about the current property market in making us understand a fair transaction for a deal. Clients get a good deal based on real market data considering the system is being updated regularly and the prices are up to the mark.

These days forecasting has a huge demand in the field of any business. Real estate buyers and investors need to have a clear picture about where the property market is heading, paving the way for them to use predictive analytics to predict value of properties in a given region enabling the property investor to make appropriate estimates using data science. Another useful method for data science in the real estate market is the usage of cluster analysis method. This method is particularly beneficial for real estate investors. For instance, in case an investor has missed an opportunity to invest in a certain property, he/she can apply the cluster analysis method to find a similar property providing the same benefits.

Another important method to determine the value of a property is the Geographical Information Systems (GIS) which helps us in getting information about the neighborhood, locality and the entire locality on which the property is based, making data science an important tool to gather information about locations and produce highly accurate maps.

In regard to AI and data analytics being widely used across industries for better understanding of consumer behavior, it has also become a very important tool for developers. Better analysis of the customer segment with the use of AI is helping developers reach out to the right channel partners and liaison better offers and deals for their customers.

Real estate investments made in the right place at the right time can be profitable in the long-run. Investing at a young age can result in high returns in the future stimulating more and more to invest in profitable assets at a young age. Rise in prices of property is a never ending discussion leading to property demand of Indian buyers causing many investors to adopt the “wait and watch approach”. Safe and good returns are guaranteed to every investor through proper planning and research done in the right direction. In order to make wise decisions and maximize their return on investment, buyers as well as sellers require Data Science, Machine Learning and Python knowledge. Hence, data science is becoming a necessity in the field of the real estate domain. Information that real estate companies receive day by day, hour after hour should be adopted and analyzed in the right way. In return, implementation of data science for real estate opens up new business opportunities. Such technical advancements would pave the way for effective building and efficient project management planning.