Do you Know you can use Analytics to Find that Perfect Apartment?
Analytics is booming and today we see it being used in traditional and non traditional sectors. One of these sectors is the Real Estate Industry. Essentially real estate can help answer the following questions:
- What kind of data is available?
- What kind of analysis is possible?
- What are the Challenges in the market currently?
- How can we improve the customer experience?
- How can we use the insights from data to improve profitabilty?
Let’s discuss in more detail how Analytics can make a difference in helping Agents/Owners and buyers make a more effective deal.
The most influential factor for a property to be buyable is the lifestyle in the property’s locality. Information related to Safety/Security of the area, Accessibility, Availability of services and commodities, transportation facilities, Neighbourhood etc. along with information obtained from past records can be combined to come up with lifestyle ratings for every listed property.
Show the buyers only a set of properties that suits their expectations. Analytics techniques like clustering can be used in segmenting the properties based upon parameters like property pricing, Lifestyle rating, type of house (individual/flat), in-house facilities (furnished/unfurnished), locality or any combination of the above.
3. Identify Stale information:
After an exhaustive search for hours, it’s disappointing to find that the 2-3 shortlisted houses happen to be no longer available. Why is the house even listed still in the site? The Owners/Agents usually don’t bother to remove it.
Analytics can be used to identify stale information. A predictive model can be developed that can identify a set of properties that is more likely to be stale. For example, a property with a high lifestyle rating, good in-house facilities and nominal cost is most likely to be sold out in the first 10 days. Measures can then be taken to confirm the availability of these specific properties.
4.Predict trends of Real Estate Highs/Lows:
Predictive models can be built based on a Country’s economy, political condition, customer’s buying behaviour etc. to predict best times to buy/sell a certain property.
The insights that Analytics can provide to Owners/Agents:
Mining the available data can provide insights about customer’s buying pattern using which suggestions can be provided to owners to improve the saleability of their properties. For example, suggestions based upon insights like “houses that have basic furnishing are more likely to be chosen than the ones that are unfurnished” can help owners upgrade their properties. Suggestions can also be segmented based on locality/type of property/cost etc.
2.Predict trends of Real Estate Highs/Lows:
Predicting the times of highs and lows will also help Agents/Owners make better decision about when to sell/rent their property.
Companies like Google Analytics, CIO, Skytree among many others work towards providing Analytics solutions in the Real Estate market. A Start-up from India called Housing.com – A Real Estate Portal is a good example of how effectively Analytics can be used in the real estate market. The Data Scientists at their Data Sciences Lab are constantly churning data & algorithms to map out new features & tools to make sure that the next choice customers make is better and smoother.