A Real Data Case Study

Howdy, everyone. I have been a bit busy working on my hard skills. Most recently, it was a Google Data Analytics Certificate. I have been a part of many data visualizations over the years that have answered many business questions. Sometimes, it answered questions that were not asked initially.

This is what I love about data analytics. It caters to the curious.

Data analytics can cut through assumptions, prove theories right or wrong, and save stakeholder resources.

What I am going to do in this space is try to answer real estate questions with data. Hopefully, it will be questions that you have had as well.

Where should I market my property?

This is where I will be spending most of my time. It has been a question I have been trying to answer for over a decade with facts and not opinions. We are in a multi-channel advertising world and need to know the suitable advertising medium and where to target. In this case, I spend more of my time on the latter.

Table of contents:

1. Story 📖
2. Goal 🎯
3. The Data 🧩
4. The Answers 📊
5. Improvement 🧰
6. Business Idea 💡
7. Conclusion 🔚

Story

Once upon a time, we launched a slick new digital advertising tool that allows agents to easily place listing, open house, and personal branding ads. I only got only one question, and I felt it was a bit of a gray area:  Where do we target?

Typically, two general options in targeting real estate ads are a 15-mile radius and choosing zip codes or cities. "Which method should I use?" came up a lot. I would tell agents that if you are trying to target people fleeing San Francisco, then targeting SF would be good, or second home markets in Tahoe might target the Bay Area, but I couldn't give much more detail. It hurt my soul not knowing exactly. Even if I was right, I didn't know for sure. So agents took this minimal information and ran with it. I heard exciting theories like Livermore, CA buyers come from Fremont, CA. While this might be true, how do we know?

This started my journey to find actual data on migration/mobility habits on a city-by-city level. I got close a couple of times, but something was always missing, like the age of the data or scale, or it only showed state to state.

Another assumption we make is about who uses what to view ads. Rural communities use the radio still, and seniors communities like print media more than the Internet. While there is a large chance this is true, wouldn't we like to know for sure? Maybe one gets more eyeballs, but something else converts better. As we go through these case studies, questions like this will be answered eventually.

Fast forward to now, I have been studying data analytics and am determined to try to answer this question again. Armed with new tools, programming languages, and new data sources. I want to show you what I have found. It is not perfect, but it is the beginning of something that could be used in the future.

Goal

The goal is to have a tool that Realtors can use to gain insights on where to market properties.

There are many ways to expand on this:

1. What type of marketing works best by age and location?
2. How much should I target inside my area versus outside traffic?
3. Can this data be used to simplify the advertising purchasing experience?

The Data

The data I will be using is US Census Data. It can be found here. I have many other ideas of data that could be used, and you will see that in the improvement section, but we will be going with what is freely accessible and publically available for this version. Unfortunately, Census data is only as good as the collected time frame and the participants' entries. I don't know when you last filled out a Census, but I can't remember mine.

The elephant in the room: The data is from 2015 to 2019. The answers shared will be from that timeframe. Many of us have massive assumptions about moving habits before and after 2020 due to COVID-19, but this data will not help. In the improvement section, I will go deep into other data sources that are way more current that I either don't have legal or financial access to.

I built a data dashboard in Tableau Public. Take a look at it here. You can choose a general metropolitan area and see where people are moving from. Not all metropolitan areas are there, and it is not shown if they came from a different state. For example, you might want to see Danville, CA, but your only option is larger regional areas in the Bay Area. Or if you choose Reno, NV, or Austin, TX, you won't see what state people are fleeing from in detail.

The Answers

While the results were limited to the data supplied (2019 Census data), I found a lot of insights.

Examples:

1. People moving from San Francisco mostly came from San Jose, Sacramento, and LA.
2. When something says "Outside Metro Area within U.S. or Puerto Rico," that could be anywhere the Census didn't track, not necessarily a different state. So when I looked up Reno, Nevada, the primary travel from destination was   "Outside Metro Area...".
3. Speaking of Reno, NV, the next closest Metro Areas people moved from were Las Vegas, NV, Sacramento, CA, Carson City, NV, and Phoenix, AZ. Looking at it confirms many political and economic assumptions that many of us have.
4. Not all cities and towns were clearly represented, so I couldn't look up places I know well, like Danville, CA, or Brentwood, CA.

Improvement

What I have found only scratches the surface. The data was old, and we just got through a pandemic. For this to be more viable as a targeting tool for real estate marketing, we need more accurate data and different data on the most appropriate method for marketing to a subject region.

There is better data. I do not have the resources for it, but the US Postal Change of Address database seems more reliable. Not everyone fills out a Census,  but they must get their mail. The US Postal Service has The National Change of Address Link, which one can subscribe to for over 160 million entries. Most people use this for mailer marketing, but getting to and from data would really answer the main objective of real estate targeting. This costs a lot of money which this researcher does not have right now.

Another great data source would be if you are a brokerage with transaction management data. This would give you facts on your company's transactions. But a big problem would be messy entries like abbreviations instead of writing out the full city name. The same thing applies to the lead source. If the data isn't a drop-down menu where every option is already typed out, humans will write in data however they remember it. This can be solved with better form field control at an admin level.

Another question that just this 2019 Census data doesn't answer is the appropriate marketing method. Factors to consider are age, access to high-speed internet, and which mediums convert best. That would be a project on its own, but if I could combine the best medium to market and best area to market in one visual, users of the tool will have a very clear answer for success.

Using the visualization tool was helpful, but there are limitations to what visualization tools can do. For the most part, they are read-only. They are meant to be read and applied somewhere else. You can have the best data and valuable insights, but action must be taken elsewhere. This leads me to my next section.

Business Idea

Imagine a near-perfect set of data, as live as possible, showing where people are coming from and what method you should use for marketing to them. Then a tool for putting in your subject property or marketing territory to grow your customer base. This tool will be visual and impress you by showing you what you didn't know or confirming your gut feeling on what you knew already.

The ability to buy digital, print, radio, or TV ads right through the product will make this truly special. This isn't as far-fetched as it sounds. Middle-manning ad tools with APIs (bolt-on tools from other companies) is possible.

Knowing that a senior community in City-X consumes all its content in print, digital, TV, or radio and then being able to order those ads on the same screen is the real trick. Or, to find out that everyone was wrong about seniors preferring print and pushing a huge digital campaign for the win would be an awesome customer experience.

There are two ways for this to happen. The product described above as an all-in-one product/service. Or the data and insights are licensed to existing ad product vendors for them to integrate into their ad-buying tools. I believe the latter is more likely since these ad companies are already built for integrations for lead/list sources. Then, this data tool would be a subscription as a business-to-business product.

Conclusion

Okay, we are done for now. While I didn't make any major breakthroughs, I figured out how to do it with more resources. I have demonstrated the ability to find data, analyze and scrutinize it, visualize it with an easy-to-use interface, how to apply the insights, and, most importantly to me, how to improve and monetize the results.

At the end of my certification process, I was supposed to do a data case study on either some boilerplate project the educator supplied or my own. I couldn't get this data question out of my head, so I was determined to stick to it. I realized I do not have the resources to make it happen on my budget, but I know what needs to be done. I also know how to monetize a data project for a business. This is what I love about data. Sometimes it can answer day-to-day business questions

or open up new revenue streams while helping its users.

This is not the end of my data case studies. I think the next will be about internet access and usage. I want to see if those people in rural areas really don't have internet. My theory is that mobile data hasn't been considered. Another factor not considered is aging individuals getting into technology because they were locked up during the pandemic. I want to see the hard data on this.

Thank you for making it to the end.