Predictive analytics delivers homes from hindsight to insight to foresight

Predictive analytics is proving the real game-changer for many businesses globally and the real estate industry is not in the dark.

When historically, conventional analytical methods and data sources in this sector made it challenging to harness data for quick, actionable insights, the new and unconventional data sources are emerging to identify valuable patterns, benefiting both real estate professionals and consumers.

Dave Garland, a Partner at Second Century Ventures and Director of Strategic Investments for the National Association of REALTORS defines predictive analytics as being, “The analysis of extracted data, using old data to predict the future.” He further notes, “The big difference now is we have new data points that we can apply to our industry. We’re moving from the idea of hindsight to insight to foresight.”

When this may seem far-fetched for Africa, a country that is best termed “Net importer of tech”, Mizizi Africa Homes will soon deliver this dream to your doorsteps.

The developer announced that Mizizi is deploying big data to boost efficiency of its operations and will soon offer custom solutions to prospective homeowners. Its intention is to seek and understand the existing and new clients better, by analysing their dream home preferences, purchase history and financial status to help it make more intelligent recommendations on personalised products and pricing structure.

“Our plan is to ensure that we provide customers with the services they really need at affordable cost. We believe by analysing and processing information about homeowners we will be able to personalise our offerings,” said Mizizi Africa Finance and Operations Director, George Mburu.

Below are ways in which big data and predictive analytics will disrupt your real-estate experience;

Finding the best investment property

Location is a significant factor when looking for an investment property. Predictive analytics help investors in choosing the best place(s) for their investment properties. The heatmap analysis can further tell the best area(s) based on metrics such as property value, rental income, and occupancy rate.

Predictive analytics also reveals the essential features of the most valuable properties in a particular housing market. For example, predictive analytics may show that to get the most return on property investment, a townhome with two bedrooms would be an optimal choice in a particular neighbourhood.

What’s more is that this technology is advancing so much that it can forecast trends in the market as well. Real estate investors always want to buy properties for cheap in up-and-coming neighbourhoods. The problem has been figuring out which neighbourhoods are improving and which still have a ways to go. Big data can potentially make these recommendations based on historical data and current investment trends.

Be ahead of the market as a property investor.

Advancing future of the industry

In his article ‘How predictive analytics is changing the real estate market’, Adrian Fisher, the Founder and CEO at PropertySimple, notes that predictive analytics is barely scratching the surface of the real estate sector. He however observes that the impact of this technology is apparent.

“There’s no doubt that predictive analytics will drastically change the landscape of this market in years to come for residential and commercial real estate and on both the buying and selling side,” reads the article in part.

Investors and agents alike are starting to pay attention to predictive analytics in order to not lose out to competition with tools becoming so commonplace that they’re no longer just in the hands of data scientists. Understanding how this technology works in transforming the real estate market is crucial and its potential is expected to bear even more moving forward.

Asked how predictive analytics helped real estate customers, Mburu said, “It fosters and strengthens customer engagement, trust and loyalty in line with the commitment to involve prospective buyers in the entire construction process and offer competitive prices on properties. 

This is a whole new experience.

Analytics will also help prospective developers plan on new development sites.

While home builders will identify the patterns of successful local developments and work to reproduce those results, builders will be able to analyse in-demand property features and design homes that include the features, expediting the selling process.

Big data provides information on what consumers demand in order to install the best selling features.

Property valuation

According to RISMedia’s 2017 Real Estate CEO Exchange, the most indispensable predictive analytics tools five years from now are those that help agents communicate on a deeper level with clients about property condition.

Home improvement ROI is ‘the topic’ that potential sellers often discuss with agents as sellers want to know how the costings for finishing a basement or renovating a kitchen is and generally, the value of their home. By analysing the upgrades and improvements of local homes, predictive analytics can identify what kind of properties local buyers are willing to pay more for, or the condition they expect at a specific price.

Agents can also use the same information to recommend specific homes and give this information to sellers early on so they have the time and budget to make necessary improvements.

Mizizi, leveraging on these technologies, emerged the best in two national real estate awards in 2019 for transforming the way it engages and offers value to its clientele. It was ranked as the most improved real estate developer in the use of digital solutions during the third digital tech excellence awards held on December 2019 at the Intercontinental Hotel, Nairobi.

This was shortly after the company was voted the most promising housing developer in the Kenya, during the second annual Real Estate Excellence Awards 2019 held in Nairobi.

Under their new plan, Mizizi will monitor social networks using big data algorithms to complement traditional customer history in collecting more information to support predictive analytics that will guide the company in making better business decision.