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1. Data Analytics Is Extremely Useful for Recruitment
Recruitment remains to be one of the most important components of growing a real estate agency. It also remains to be one of the most challenging, especially considering the competitiveness of the space and the amount of resources needed to hire a good agent.
With the help of technological tools like Brokerkit recruiting software, the entire process can thankfully be made a lot easier. The key is the suite of features that allow recruiters and hiring managers to access plenty of valuable data from leading industry sources like BrokerMetrics. Brokerkit also comes with a built-in lead segmentation tool, which is particularly useful for hyper-targeting candidates based on criteria like location and production performance.
2. Data Science Can and Should Be Applied to Pricing
There are many factors that can affect the price of a property. These include its location, physical condition, amenities, historical performance, and even how many times it has been put up for sale. As such, it can be difficult to come up with a figure that both seller and buyer can be happy with.
This is where data science and analytics come in. Techniques like hedonic pricing which identifies key attributes of a product being sold and assigns a price to each can give real estate agents the ability to come up with more accurate estimates that are based on concrete values. There are also tools and sources that can track a property's repeat sales prices over time, so that agents can come up with a reasonable average.
There are also a few more data points that should be included in price calculations, including tax rates, government intervention, rental price averages, and the cost of services in the area. Many real estate agents will also find that creating multiple price indices is more helpful in the long run, particularly when dealing with sub-market pricing.
3. Cluster Data Analysis Has a Variety of Applications
Clustering and cluster data analysis are crucial for identifying patterns that can influence real estate prices across different neighborhoods, cities, and states. These techniques can also be used when comparing real estate performance in different time periods. This can help real estate agents advise their clients with regard to deciding when it is most optimal to buy or sell.
Another practical application of cluster analysis in real estate is in building business strategies. Specifically, those in leadership positions can make better, data-backed decisions as to which segments to target and what kinds of investments to make.
4. Data Analytics Can Help With Property Visibility
The internet has been both a boon and a bane to real estate. While technology has made it easier for real estate buyers and sellers to find and sell properties, certain algorithms can also affect property visibility. Of course, search engines can't be blamed for wanting to optimize their services in order to serve their users better. What real estate professionals have to do, therefore, is to pay attention to these algorithms and use available data to make their listings more visible.
Fortunately, there is plenty of easily accessible search engine data, like search volume per location, popular keywords, and search intent, that can contribute towards property listing visibility. The ultimate goal is to make a property listing rank as high as possible in search engine results pages to increase its likelihood to sell as quickly as possible at the best price.
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5. Data Accessibility Means More Geographic Information
One of the biggest, most important factors in real estate is location. Whether a company is looking to build a new structure or a family is trying to relocate, one of their primary considerations is finding the ideal place. Through data analytics, real estate professionals can capture and store crucial geographic intelligence to identify the best locations for their clients. With the right geographic information system, both pricing and visibility can also be addressed by data analytics.
Some of the parameters that real estate professionals should include in their geographic data analysis are:
- Number and location of amenities
- Transportation accessibility (including commute times)
- Migration by neighborhood
- Competitor placements
- Number of properties per area
Fortunately, there are now more sources from which to extract reliable data.
6. Data Analytics Can Help With Accurate Appraisals
An accurate appraisal is crucial for anyone buying, selling, or insuring a property to fetch a fair price. It can also help property owners save on taxes and ensure proper coverage. For buyers in particular, a proper appraisal can help speed up the process of acquiring a mortgage; in addition, an appraisal is one among many non-financial aspects of due diligence.
To ensure the accuracy of price estimations and valuations of the property, real estate agents and appraisers can turn to data analytics. This can then help customers decide whether something is a good investment or not, based on factors such as building regulations (which can affect construction costs), commercial trends, and area planning.
7. Customized Marketing and Advertising Through Data
Finally, data analytics is an undeniably useful tool for fine-tuning real estate advertising and marketing strategies. By collecting relevant consumer data, real estate agents and recruiters can build buyer, seller, and candidate personas, with customized messaging and campaigns for each.
The keyword, of course, is relevant. While there's a long list of information that can be gathered from different sources, some of them may be less important than others. It will therefore benefit real estate companies to invest in data science and analytics training, so that personnel can be more adept at processing and interpreting data.
Data Analytics and the Future of Real Estate
Considering how entrenched technology is in almost every industry, it's reasonable to assume that data analytics will also continue to play a key role in real estate. It's important for real estate companies to start as soon as possible with their data training and investment, if they haven't yet. This will allow them to capitalize on all of its benefits, as well as future-proof their business.