You know when you have to hail a cab, and you check the rates on Uber, Lyft, or the local taxi company to compare their prices? That’s you collecting data to make a decision.
Businesses do the same but on a larger scale. They use data to create targeted marketing campaigns, business strategies, and general decisions.
Like every other industry, stakeholders in the real estate sector also rely heavily on data analytics. From price comparisons to predicting future trends, data makes all the difference between a good and bad decision. Here’s an in-depth guide on how data-driven decisions can be made in real estate.
Data is at the base of many real estate decisions today. Even better, data is combined with machine learning algorithms to predict everything from price projection to buyer interest.
For example, a data-based application predicted the three-year rent per square foot with a 90% accuracy rate for a Seattle multifamily unit. A notable advantage of an application run on advanced analytics is that you can scale it easily.
For example, do you want to make decisions about capital expenditure? The same application shows you data about stabilized yields and returns on investments to assist your decision.
Similarly, you can compare the outputs of this predictive model with traditional sources, like brokers. But how exactly do real estate stakeholders use the data they collect? Here are some use cases.
Real estate is a risky business. You put hundreds of thousands, if not millions, at stake. So, it makes sense to be a bit cautious. That’s where data helps.
Let’s say you want to buy a multifamily unit. You won’t just jump right into it. Instead, you’ll gather data about the property’s age and historical price.
Since multifamily units are usually for rentals, you’ll also want to learn about past rental values and future predictions. Plus, you should consider the region’s average income and closeness to public transport, parks, supermarkets, etc.
The data will help you predict future returns on investment and know what risks you’re taking with your purchase.
Almost every business uses data to target and segment customers. In real estate, data can help you determine a lot. For example, which demographic can afford the property you’re selling? Which demographic makes up most of the current population of the area?
Knowing these things can help find the right customers for a property. Real estate businesses use data analytics to target customers with preferences, incomes, interests, and expectations that meet the property’s attributes.
How do you think a real estate agent comes up with the property valuation when they put it on the market? Is it a ballpark figure they make up in their head? No.
The figure is based on data-based calculations. For one, what is the value of other properties in the area? If properties in the neighborhood sell for an average of $500,000, you don’t want to list yours for a million.
Proper property valuation takes the guesswork out of the equation. The result? Better sales and bigger profits.
The kind of data a real estate stakeholder collects will depend on the purpose of data collection. As an example, an investor will need the following data:
- Current and past price/rental values
- Average income
- Housing demand and availability
- Property’s age
- Property taxes
- Proximity to transportation and other amenities
Besides real estate data, agents also collect information about a locality’s crime rate, traffic flow, development plans, foot traffic, population age, and other factors.
In some cases, you might also need migration and population trends. For example, if an area is known for its high-quality public schools, it will attract more families than young childless couples or elderly buyers.
Real estate businesses also collect online data for their marketing and SEO campaigns. It could include website visitor data, competition analysis, ad performance, and consumer behavior.
Online publications like Forbes and McKinsey often give their two cents about the real estate market and its trends. The same goes for news websites.
However, they don’t contain comprehensive real estate information that you can get from a site like Zillow. Here, you can find it all; price history, rental value, features, amenities, local population, price projections, and more.
But don’t forget that Zillow has millions of listings. How can you find your desired information? The Zillow scraper API could help.
Think of it as your Zillow-specific search engine. The Zillow scraper API is designed to collect information from Zillow only.
All you have to do is specify the data you need, and the scraper will collect it for you. Suppose you want to know the price projections for 3-bed homes in Harlem. Run this command, and the Zillow scraper API will show you Harlem’s 3-bed homes’ price projections in a structured data format.
In the post-pandemic era, real estate is likely to boom again. Data will be an important factor for stakeholders in the industry to target consumers and drive sales. As the Zillow scraper API speeds up data collection, it saves a ton of time that real estate businesses would otherwise have to spend on manual data gathering.