Three Ways to Harness Predictive Analytics in Real Estate
The real estate business is risky and prone to many unexpected challenges. Realtors have to plan property pricing, find potential buyers and sellers, work with brokers and optimize brokerage charges. People are getting more tech-savvy as new advances hit the market. Data is now available at everyone’s fingertips. Consumer demographics, housing trends, and property pricing history are a few areas where predictive analytics has a massive opportunity for the industry.
Here are a few ways to leverage data and predictive analytics to give real estate business a competitive edge.
1. Deciding on Property Value
To decide on the price of the property, realtors need to consider many factors, such as:
- Type of Neighborhood: Posh residential versus commercial versus business districts.
- Audience: What kind of audience do you have in the particular location? Which age group do they fall under? What’s their income size? What are their commute patterns?
- Selling price: What are your high and low prices in the surrounding areas?
- Audience home location: Does the local audience live in this location or do they travel from another part of the city?
Leveraging data for predictive analytics can help to locate where the majority of affluent, professionals and middle-income groups live and work. Their spending patterns, interests, preferences and commuting habits all contribute to a unified view of the potential customer. This can help to personalize real estate offerings to account for all of these details.
Source: Allspark from Near
If realtors are looking for a potential buyer for properties that they have in the suburbs of New York, they should look for the locations where a majority of affluent and middle-income groups live while commuting to New York. They could look at the property pricing in that location and build their target list price from there.
For example, Near’s data shows that majority of the audience in Queens, New York fall under 37 years median age, and USD 53,656 as the median income. It was also found that higher number of females (50.5%) were seen compared to males (46.22%) in the past 30 days. With more granular insight on the real world behaviour of this audience, they will be able to differentiate between Mr. X who is affluent, aged 35 and living in the suburbs of New York, and Mr. Y, a professional in the middle-income group who is the same age and lives in the same location. These insights can be used to decide which properties to show them and the ideal price points for each.
Predictive analytics can also help to show potential home buyers the future price projections for particular properties and neighborhoods.
2. Identifying the Best Use of the Land
Many factors go into determining the best use of a property. One has to weigh the pros and cons of housing versus commercial versus public use. Data can help to determine the right option for their short and long-term goals.
Data from sites such as Airbnb can show the market value of an empty residential property. They can look at coworking websites such as WeWork to see the typical pricing for office space in their location. Consumer movement data can allow to identify land that’s well suited for general public convenience. Parking lots, ATMs, parks and toilets are a few alternative uses for these spaces. Realtors can also use this information to pinpoint the specific buyer who may be interested in the property.
Predictive Analytics can be used to analyse the flow of shoppers around a commercial area. Looking at these patterns at a city level, they can find hot spots that would be perfect for public use properties.
For example, Near’s data shows that in Manhattan, New York, McDonald’s sees higher traffic than Chiptole, Burger King, and Domino’s Pizza and also 59% travelled 3.0 – 5.0 miles to get to these Fast Food Outlets. The audience were mostly Professionals and Affluent and the peak time of foot traffic was from 12 pm – 8 pm. Customer average dwell time seen at these outlets was 23 minutes. The data also showed that majority of the audience seen in Manhattan prefer banking with Citibank followed by Capital One and TD Bank. With access to insights like this, they can decide if the empty land can be used for commercial purposes or for public use.
3. Connect With Your Customers
Home buyers and sellers from generations X and Y do the majority of their property research online. They typically don’t contact a real estate agent until later in the process. Once they do, these consumers expect the agents to immediately provide additional information on the homes that would be valuable for them in making the right decision.
By leveraging predictive analytics, realtors can stay updated about consumers online and offline behavior, and understand their journey over time. This can further help to identify the consumer groups that are interested to buy homes and reach them with the right message, at the right time and on the right device.
For example, if they can identify people who have been visiting house listings in the last month, and are able to get insight into their interests in the real world using data, realtors may have a much higher chance of closing deals with them sooner than later. This can also be an input for the banking and insurance companies who will have these home buyers as their prospective customers, and can reach out to them early.