The number of connections that I have in real estate really hit me when I made the decision to buy a new house. My Facebook and LinkedIn, even my Instagram, connect me to at least 40 people in the business. I had brokers, agents, builders, and designers spanning between commercial and residential. I see them pop up all of the time but I don’t see any of them talking about Real Estate analytics.
As I looked at these connections, I started to see a potentially untapped market. After all, who really thinks about real estate analytics? Real estate is land and analytics is data; the two just don’t seem to mesh. Real estate is a multi-billion dollar industry and I really began to think about how analytics could influence it.
Armed with my curiosity, I turned to the Internet to start looking for data to test against. It took some time but I found success across various open-source systems. With a few well-placed lines of code, I had populated a development CRM with all of the necessary data. I had Contacts broken into Buyers and Sellers, Leads, and Properties. I even had Account-data for industrial property.
For my case study, I used Salesforce CRM and Einstein Analytics. If Salesforce and Einstein are a bit out of your pricing range, you can easily use Tableau and connect to various data sets. Both tools work great, both are owned by Salesforce, and both have a number of ways to connect to external data. I did a few trial runs of the data through Tableau and got similar results, though my testing was not as in-depth as in Einstein.
Real Estate Agents
We’ve all heard the term “big data” and to say it is a fad would an understatement. You don’t have to look hard to see that we’ve always had data sources that could be described as “big data”. Big data has become the buzzword of the decade as we move to a more data-driven society. Even the most unsuspecting devices are collecting thousands of data points every day and storing them to await possible consumption.
As a Real Estate Agent, you’re probably wondering what big data has to do with you? You already deal every day with data metrics such as lot size, dwelling size, list and appraisal price, and more. Now that you’re thinking in those terms, I’m sure you can write down a long list of items.
Once you have your mental list let’s take it one step further. What if we added distance to local schools to your list? We could also add how much light a house gets each day, the average yearly rainfall, or distance to nearby amenities. All of this data, and more, is available either through personal data input or large real estate databases. Realtor.com and Zillow both collect hundreds of metrics and it is all accessible through their API.
Armed with our data, let’s look at how real estate analytics can help grow our business.
Working your leads.
Real Estate Agents typically deal with two types of leads on a daily basis: buyers and sellers. Generating and working leads is critical to success in a business driven by commission and networking. In addition to looking at the lead type, we’ll also categorize our model into two categories. We will look at Lead Generation and Lead Nurturing. Lead Generation is the term applied to get new leads into your CRM or system of choice. Lead Nurturing is taking generated leads and trying to nurture them towards closing. Between both of these, you might also look at Lead Qualification. Straightforward, this is taking a new lead and seeing if it qualifies to be a potential conversion to a client.
Lead Generation
Let us start by looking at the first step in the Lead process: Lead Generation. We already know the traditional way a Lead might come into your systems. You have a list of people you can send mailers to, perhaps an E-mail list purchased or cultivated through your website, and cold calling.
What about non-traditional lead generation? Thanks to technology and databases, we have a number of potential leads at our fingertips. The logical place to start, and a good first step, is with real estate listing sites. Think Zillow or Realtor.com. These sites not only allow potential leads to viewing a property, but they are also collecting data points on all of their visitors. Many sites make this data available for a low flat, or recurring, fee to end-users.
Apart from the actual data that builds the lead (email, name, address), there is supplemental data that you can access. These data points are critical to getting better lead scoring from AI for qualifications. You already know who your leads are at this point. Imagine if you also had access to their demographical data, social media data, financial data, and even current mortgage information.
From that list alone, I can already construct potential buyers based on social media and financials. I can also construct potential sellers by looking at their mortgage, home valuation, financials, and pitching them on if this is the right time to sell with supporting data.
This may seem like a lot of work, but this is the first example of how analytics can help you as an Agent. Give any of the various analytics platforms access to your data sources and tell them what you want. Don’t get bogged down in real estate analytics. Let the machine run all off the data, looking for relationships and highlighting important factors in a solid lead, while you focus on more important business tasks.
Lead Qualification
Having leads is great, but you now have a machine processing billions of rows of data every day to generate new leads for you. How can you possibly go through all of the leads that are likely to be created? You might find yourself spending over half your work week trying to qualify leads and leaving less time for other tasks.
That is where Lead Scoring comes into play. We will talk about this, again, when we look at Lead Nurturing. Lead Scoring is a way for you to assign a number, sometimes a percentage, to a Lead that has meaningful representation. In this case, we would score a lead to determine if it is qualified or not. Scoring is another task that, largely, can be handling in your real estate analytics strategy.
We want to take the same data that generated the lead, possibly combine it with new data, and assign a score to each lead. Our analytics platform can look at a leads neighborhood, family size, browsing history, and so much more to determine how likely they are to buy or sell a home. The more data you can feed it the more accurate your scoring will be. The power of analytics is that it will draw relationships and assumptions for you so the sky is the limit on how much data you feed it.
Once you have a lead scored, you have to take action on the lead. Salesforce Einstein can automatically move a lead to qualified or not qualified based on scoring thresholds. With Einstein or any other project, you can also do this yourself. This helps you to stop wasting time on trying to qualify leads, and jump straight into working leads that have real value.
Lead Nurturing
Once you have qualified a lead, your hope is that it converts to a buyer or seller. Lead nurturing is where an Agent should spend most of their time and, with the help of analytics, it becomes easier too. Once again, we will look at Lead Scoring as our marker for potential success. This time, we will score a Lead-based on their likely hood to convert to a buyer or seller.
Part of nurturing a lead involves communication. If you are using any form of a CRM tool, you should able to easily attach activities to a Lead. These might include phone calls, E-Mails, SMS messages, outreach through social media, and any other type of contact.
Not to shamelessly plug Salesforce, again, but last year at Dreamforce I watched an amazing demo for Marketing and Einstein. Salesforce was able to combine real-time analytics from Einstein with their Marketing platform to generate unique marketing material to each person based on an analysis of the data. The material was targeted to each person to better influence them to become a customer. Can you just imagine the impacts real estate analytics could have on your marketing strategy?
The other side of your outreach strategy is the response. Did the lead open it, click any links, or reply back? All of these actions are additional data points that can be stored in your CRM and used in your analytics strategy. Any interactions and data you can tag to a lead will further dial in your lead score.
Eventually, you’ll either find that a lead isn’t going to convert or you’ll meet your first threshold. This first level is all about property. If we look at a seller, we might send them a full valuation of their property. We might also send them some MLS listings cultivated by your analytics engine so they know they won’t be homeless. If we look at a buyer, this would be MLS listings specifically targeted to them based on all of the data we analyzed.
The responses and interactions your leads have at this point will move the needle a little more on your lead scoring, but ultimately a lead has two places they can go from here. They will either convert or fall out.
From renter to buyer
At this point, I’ll be honest with you. The amount of data collected on us as individuals are scary. When you really start to grasp how many databases are out there, with varying degrees of information, it becomes mind-blowing. However, since it is out there, we might as well use it.
Let’s look at a renter who we want to convert to a buyer. These leads easy to obtain because rental data is easily accessible. They are also easier to convert if you can target them correctly. Take John Doe who has been a renter for a number of years. We will play out what the journey looks like.
John’s journey from renter to buyer
As an Agent, I have tapped into as many data sources as I can for lead generation. One of these data sources gives me access to local renter data. As my analytics engine crunches through the data, a lead hits a score of 95% and automatically converts to qualified. I decide to work the lead: John Doe.
After reviewing what I know about John, I determine he is married with two kids. Living in an apartment can’t be fun. The first thing I key in on is John’s rent. I might have some financial data that influences this number, but largely I will consider this the upper limit for what John can afford. This becomes his likely mortgage payment. He may go higher or lower, but I don’t know enough about John to understand why he lives at this one building and pays this one rent payment.
Now I want to look at square footage and the footage price. This is valuable because I know that John probably can’t squeeze into anything smaller than his current unit. Having a price per square foot also allows me to add value to any recommendations. If he is paying $2/sq. ft, and I find a house for $1.50/sq. ft, I’ve added value.
Taxes are my next stop. Taxes have some influence on rent but they definitely have an influence on buying. If John has to start at his monthly payment and back into a mortgage, we definitely have to consider taxes. Higher taxes will lower buying power. With all of these details, we now know what he can afford based on taxes and start to generate listings. These listings will group by zip code for easier reference. If we are lucky to be data-rich, we might have financial insight into a likely downpayment. We don’t want to bank on what could be a nest egg or a college fund, but we can create a second group of listings based on a possible downpayment.
We want to really hit home with John. Now we have two sets of listings but are they ideal for John? What amenities are close to John that we might want to consider? What is missing that might be beneficial. With kids, this could also translate to school districts, community pools, and others.
Accounting for all of these data points, we now have two good lists of properties. I might go ahead and apply additional filters to cut out listings in a high-crime neighborhood. Some families are hesitant to buy a house with a pool if they have small kids, so I could further segment both lists to pool and no pool.
Last, I want each list to put the most likely properties at the top. One sorting filter might be a value-add, putting bigger houses or newly remodeled at the top. If we know where John and his wife work, we might try to sort by commute distance. Perhaps we also want to add in school distance, or where a school falls between the work and home commute.
Back to reality
All of these things are great, but the amount of work can be daunting. Looking at just that list, and there are so many other data points you could consider, gives me a headache. Can you imagine having to try to manually compile that data and manually translate it into listings?
Thankfully, you can use real estate analytics to do this work for you. With a few inputs from you, your analytics engine can pick up the lead and start creating your property lists. Now you have tailored properties, that likely fall within the renters budget, to send in your initial contact.
Cold calling might result in some interest, but many renters think that buying isn’t in the cards for them. Insert the tech-savvy realtor who comes to the table with a list of houses that meet 80% or more of the renters’ needs and is within their buying power. You’ve already overcome the mentality that they’ll be a renter for life, or now isn’t the right time, and have them actually considering the listings you’ve given them. Now it is just nurturing this lead to conversion to a buyer and getting them to closing.
Market Analysis
The last topic I want to review is market analysis. With COVID-19 hitting hard the global economies, many people wonder and fear about the state of the housing market. Is it the right time to buy or sell, how are mortgage interest rates, and will I have a job next week? we might not be able to answer all of these questions, but we can certainly answer some.
Using real estate analytics and available market data, you can proactively include market factors in your direct marketing as well as a market analysis in your newsletter or website. Market conditions and risk factors are two of the common ones that you can report on.
You could also look at market trends. How is the market doing by state, city, or zip code? What about market trends by the school district? Your analytics engine is doing the heavy lifting so it a stretch to have multiple versions of market trends filtered by varying factors.
Perhaps you want to look at listing or rental trends. How much inventory is on the market, how many people are renting, and how many leases are coming up for renewal? Is this the right time to influence a lead to sell or a potential renter to buy? If the inventory is low, you don’t want to waste their time or yours.
How about property valuation for unlisted properties? Your analysis turns up a neighborhood with long-time home owners living on property that has increased in value. If the inventory is right, and they haven’t taken out a second mortgage on their house, it might be the time to sell and make a large profit.
Wrapping it up
In this article, I touched on the Real Estate Agent and how analytics could make a difference in their business. There are obviously so many other data points that could be considered, and applications they could be applied to, then what was covered here. We also did not cover the art of the possible for brokers, builders, investors, or commercial real estate.
Buyers and sellers today are using technology to help navigate to the best deal. Agents should be looking to do the same thing. Analytics can help offset increasing competition, reduce pricing pressure, and lower operating costs.
All of this may sound daunting, but here’s the good news: You’re not alone. QuadraByte has the consulting experience to guide your Real Estate business towards a custom solution to fit your needs. From data architecture to facilitating analytics, we will take you to where you need to be. Contact us if you want to learn more.
