Tax Liens are Counter Cyclical to the Economy

Over the last few months I performed an in-depth analysis of IRS data on federal tax lien filings from 1990 through 2013.  This data mining exercise yielded some very interesting results and revealed some of the drivers behind the volume of federal tax lien filings.  The analysis was so helpful to my company that I decided to share it and so part 2 of my blog series “What You Need to Know” continues.

The volume of federal tax lien filings is generally counter cyclical to the economy.  When the economy is doing well, lien filing volumes decrease.  The time series shows us that the number of liens filed declined significantly from 1990-2000 and this trend started to repeat itself again with a steady decrease beginning in 2010.  Both of these time periods fall during economic growth and bull markets.  However, bull market alone wasn’t a strong enough of a correlation since it did not apply in the mid 2000’s. I searched on for other relationships.

It turns out that unemployment rate is one factor associated with economic growth that is also highly correlated with the number of tax liens filed.  On the right is a chart depicting the relationship between federal tax liens and unemployment rate.  The lower the unemployment rate the fewer tax liens filed and the higher the unemployment rate the more tax liens filed.

Changes in unemployment rate tend to be correlated more strongly than market trends, so watch unemployment as a tool to help you anticipate fluctuations in lien volume.  There were over 100,000 fewer tax liens filed in 2013 than in 2010.  This current growth cycle is forecasted by some to last another 3 to 5 years.  In this environment, lien volumes will probably continue to decrease.  Fresh leads and good analytics are vital if your company depends on tax lien filings for success.

Seasonal Fluctuations in Lien Volume

It never fails, every September I get a call from a client saying “Hey, why is federal lien volume so low?” then every May I get a call “Hey we are swamped, why is federal lien volume so high?”  And every year I explain this is partially due to seasonal fluctuations in lien volume. Most consumers of federal tax lien data are aware of major lien disruptions such as the October 2013 federal shutdown or public holidays but they are less familiar with normal seasonal variations in lien volume.  Below is a chart aggregating the monthly variation of lien volume over the past 4 years.  Note that October is skewed significantly downward due to the 60% drop in lien volume during the October 2013 government shutdown.

Between 2010 and 2013, the IRS has filed an average of 16,600 federal tax liens per month.  On average they filed 15% more in May (18,900) and 18% fewer in September (13,600).  This swing in lien filings can have significant business implications when you rely on federal tax lien filings for sales leads.  Fewer leads typically means less revenue so predicting lien volume is quite helpful in estimating cash flow. So in the end, my time spent with clients discussing seasonality is well spent.  Developing models to predict lien volume turns out to be a great planning tool.  Let’s just hope there are no more government shutdowns on the horizon.

Connecting to your Sales Leads

Accurate phone numbers can be hard to come by.  Phone number databases sold by the big boys are expensive and outdated.  And searching the web yourself can be time consuming and tedious.  Append rates and accuracy vary widely.  Is it worth all the effort? From my experience, taking more time, finding more hits, and suffering a few more wrong numbers can lead to gold.  If it’s difficult to find a number then that means your competitors are having the same problem. Going that extra mile to append could be your key to success.

So where are your best bets to find business numbers?  From an append rate standpoint, company websites provide the highest percentage of found numbers followed by search engines and business phone sites.  The lowest append rate usually comes from phone number databases at about 30%.  In general, I believe a hybrid approach using multiple sources and looser (but reasonable) matching criteria are vital.  A few percentage point increase in append rate could make a huge difference in your bottom line.  Again the more difficult to connect with a sales lead, the higher probability of a sale.

But let me guess, you send mailers instead of calling leads. Well, the same problem exists here.  Addresses provided with sales leads can be notoriously inaccurate, from misspellings to simply being the wrong address.  Companies often put the address of their accountant, lawyer, or some other person on their formation documents and vital records. The address on the company’s website or one of the business sites is more likely to get your mailer where it needs to go.  Again a hybrid approach rules the day, and sending a couple of extra mailers to multiple addresses can really be worth it.

Ironically, all of this gets easier when you make a lot of calls or a send a boatload of mailers.  Statistics are your friend and simply tracking phone disconnects and returned mailers can quickly help you refine the accuracy of your system.  Our mantra is append more, analyze, and refine. At the end of the day, more connections means more sales.  It’s a competitive industry and the strategies that focus on the hard to find phone numbers and addresses often give companies the edge they need to win more clients.

Adventures in Predictive Analytics

In my last blog, Eliminating the Guessing Game of Yesterday, I shared my recent obsession for business intelligence and predictive analytics. ExtraktData’s technologist and I have been experimenting with our data a lot since then.  Our main goal is to develop a model that can predict the number of federal tax lien filings for the coming month.  That means in any given month we can make an educated guess on how many liens the IRS will file the following month.  For us and many of our clients, sales revenue is highly correlated to lien volume.  Predicting lien volume helps predict sales revenue and that’s quite valuable to our planning process. The prediction models are still in the works so I do not have any results to report, but rather some insights into the building process.

The Data Set

The data set for these models consists of federal tax liens filed against businesses between the years of 1990 to 2012.  Federal holidays and weekends were removed to give an accurate count of how many working days each month the IRS employees had to file the liens. We purposefully held back 2013 data, so we could test what the model predicted versus what actually happened in 2013.  As we develop our prediction model we will be testing numerous hypotheses.

Hypotheses

1. Each holiday has a greater impact than just the loss of a workday because people often take extra time off during those time periods.

2. December and the summer months are generally vacation months.  Therefore, there may be seasonality based on vacation time.

3. A relationship may exist between the date a business has to file their annual or quarterly taxes and the volume of liens filed.

4. Number of workdays in a month effects lien volume. Everything at the IRS isn’t automated (the IRS is not staffed by robots) so fewer workdays equals less liens filed and vice versa.

5. Because of the IRS Fresh Start initiative (implemented at the beginning of 2012), looking only at historical data for liens above $10K is more predictive than looking at all the data.

6. The above hypotheses combined with other factors (discussed in previous blogs), such as economic cycles and unemployment rate, will allow us to predict future lien volume.

Our experimentations are definitely a process, but one we are very much enjoying.  What’s the point in having all this data if you don’t explore the possibilities?  So keep following our geeky adventures and let’s see what we find.  We’re not promising miracles, but we will share all the same.

Eliminating the Guessing Game of Yesterday

Companies are collecting leagues of data every day.  Typically, that data just sits in a warehouse, with the enormous task of analyzing it and implementing the findings.

A recent business intelligence (BI) class has retooled the way I view data.  Until I took the class, I was only concerned with collecting data and shaping it into a nice, neat, little package to sell to customers.  I am just now diving into the world of BI and predictive analytics, and I’m not looking back.  I had heard the unavoidable buzzwords for years, but never paid attention until now.

BI is shifting.  It’s moved from being something that was nice for companies to have to a necessity for competing in the marketplace.  Businesses can make smart decisions based on data, eliminating the guessing game of yesterday.

With the constant influx of data firms collect daily, using BI to process that data can increase efficiency and effectiveness and better help them understand customers’ wants and needs.  Companies use analytics to save money on marketing by targeting customers and potential customers with advertisements that lead to revenue.  The advertisements they send are “smart”, therefore have higher success rates than advertisements not based on analytics.  For example, Target uses predictive analytics to figure out when women are pregnant and estimate their due date to within a small range. Is this creepy or amazing?  My inner data geek seems to win out on this debate.

After sharing my excitement and passion (my boss calls it an obsession) over the electrifying world of BI with my company’s founder and technologist, we decided to take a closer look at our own data.  We have data going back over 23 years and collect more every day.  That data has to be useful, right?

Over the next few weeks, we’re going to start experimenting.  Maybe we can predict next month’s volume of tax lien filings?  I’m not looking for miracles, but I will be blogging about the results here.  If you like data, keep reading to follow our geeky adventures – the good, the bad, and the ugly!