The rise and fall of Charlotte’s real estate bubble
Charlotte’s real estate bubble doled out huge profits to some, financial ruin to others, and a one-two sucker punch to thousands more as it quickly turned phantom riches to rags. Are there lessons to be learned from this? More profoundly, is there a crystal ball that accurately identified the bubble’s launch date, burst date, and final deflation. Yes to both. Why bubbles are difficult to track The industry that typically feeds the media interpretive real estate data has a vested interest in emphasizing the rise of home values, and minimizing deflation of the same. Consequently, it was common during the bubble for the media to substitute year-to-year data with month-tomonth data to accentuate the desired perception. As the bubble’s existence became undeniable, the media increasingly turned to analysts outside the industry who had no vested interest in creating perceptions. Shock waves riveted the market as headlines indicated that the historical relationship between a community’s median income and median home value indicated we were closer to the bubble’s top at a time when most believed we were closer to the bottom. Some analysts discounted the use of this predictive tool because local real estate commonly defies national norms. Nationally, homes have historically sold for three times the local median income. But in San Diego, Calif., for example, the median home is currently listed at 6.65 times the median income. Indexing a home’s market value to national norms is simply not a reliable indicator unless local home values have historically conformed to those same norms. How to identify and quantify a bubble The author of this article, a Realtor with a background in software engineering and database analysis, recently conducted a research project to determine if software could be created that would have clearly identified and quantified Charlotte’s bubble. Ironically, there is a tool already in use that did exactly this, but the tool is commonly utilized by the stock market rather than the real estate market. Trend lines Stock market technicians rely heavily on linear regression to quantify the present and predict the future. The process uses historical data to create a “trend line” from which prices bounce up or down. The resultant trend line is extended into the future to predict the future. I analyzed nearly 200,000 sales transactions spanning 25 years using linear regression to create 3 trend lines for Charlotte County — one for singlefamily homes, one for vacant lots, and one to simulate the trend line that would have been created as of January 2005. These are represented in graph 1. The beginning Take a look at the yellow line. It represents the median sales price of single-family homes from March 1983 to January 2005. The dotted line represents the resulting trend line that would have been created as of January 2005. The huge, unprecedented rise of the yellow line (representing home prices) above the dotted trend line (representing the historical norm) clearly indicates that by January 2005 we were already riding high on a bubble. The blue line is a continuation of the yellow line. It depicts the median selling price from February 2005 to the present. While those of us on the ground were debating whether or not we were in a bubble, the blue line (representing median price) shot up vertically like a rocket relative to the trend line. The proof we were in a stratospheric bubble was overwhelming as history proves prices nearly always return to the trend line. Where are we today? Together, the yellow line and blue line represent the median price of single-family homes during the past 25 years. The solid black line is the up-to-date trend line generated by this data. According to the trend line analysis, Charlotte’s bubble was launched in October 2003. That’s the month that median home prices began a nearly vertical blast off from the (dotted) trend line generated by real time data current to that point. The final deflation of the bubble occurred in February 2008. That’s the month the median home price fell below the 25 year trend line. Are we at the bottom? Our single-family housing bubble is completely deflated. Does that mean we are at the bottom? No! The only conclusion we can draw from this is that Charlotte County homes are now in line with local historical norms, as indicated by the current trend line. But the bubble itself demonstrates that actual prices can deviate wildly from the norm. It is evident that over time, prices return to the trend line. But in the short turn, the market is influenced by emotion, greed, speculators, exuberance, capitulation, supply shortages, natural disasters, the herd instinct, and external market conditions such as the subprime meltdown. Only hindsight can determine where the bottom is. The bubble’s dead. But the same influences that temporarily shot prices high above the trend line are also capable of driving them below that same trend line. Is this the right time to buy? If you are gambling on your ability to recognize the bottom in real time, or simply can’t avoid the speculative fever that was so much fun and profitable during the bubble’s ascent, this may not be the time to buy. There are still external pressures depressing the market that have not made their way to public awareness. For example, lenders have been abruptly terminating customers’ ability to draw from their existing home lines of credit since the erosion of property value may no longer collateralize the open line of credit. Many homeowners have been staying afloat by using that credit line to pay their mortgage. These pressures could precipitate an increase in foreclosures further depressing prices. On the other hand, if you have been waiting for home prices to return to their historical norm, this is the right time to buy. A conservative approach to increasing your eventual resale profit might be to “buy under the trend line and sell above it.” Another compelling reason to buy now is that you can currently buy a brand new home for less than its replacement costs as new spec and foreclosed homes continue to swell inventory. Local Realtors are now seeing appraisals on brand new homes where the appraised market value is less than the replacement costs. Lessons from the bubble Real estate is local. While many lessons learned from Charlotte’s bubble can be extrapolated to other counties, the data and trend lines used here apply only to singlefamily homes and vacant lots in Charlotte County. It does not apply to condos, for example. For that, we would need a separate analysis based on historical data for condos. But there are some clear-cut lessons we can take away from our wild ride on Charlotte County’s bubble. We have the advantage of hindsight. Understanding the behavior of the bubble will better prepare us to end up in the winners’ circle when the next bubble comes. Better tools needed We need better tools to detect and quantify the existence of bubbles. Trend line analysis, used commonly by stock market technicians, accurately identified the early stages of our bubble as it quantified the excesses at each stage of its ascent and descent. Speculators and easy money The combination of historically low interest rates, proliferation of home-equity credit lines, secularization of home loans, the lack of aversion to risk by institutions purchasing those loans, and the ease of obtaining no-down, interest-only loans, created a new generation of highly leveraged speculators. Nowhere was this more apparent than the vacant lot market. Vacant lots During the bubble, we saw almost no media recognition of the market for single-family vacant lots. Yet, they played a major roll in pumping air into Charlotte’s bubble. They were also the darlings of nimble speculators who had a much more dramatic effect on the market than their home flipping peers. During the second half of 2003, the median price of a single-family vacant lot hovered close to $6,000. By November, 2005, the median price had climbed to $60,000. That’s a gain of 1,000 percent. The vacant lot market proved to be a gold mine where rapid fire flips were unencumbered by the need for permits, impact fees, construction lags, shortages, appraisals, home inspections, buyer emotions and mortgage fees. Many high rollers bought and sold lots by the dozen — sight unseen. It was this group of speculators who appeared disciplined in the ways of Wall Street as they frequently defined their buy points and sell points before plunging in. The area’s top-producing Realtor during the height of the frenzy was a vacant lot specialist who stunned many with his spectacular sales volume. Vacant lot flippers, and their Realtors, may well hold the title as the bubble’s biggest winners. I am acquainted with four such Realtors whose annual earnings exceeded that of the president of the United States. And on March 17, 2005, The Sun published a column (“Average Joe Becomes a Real Estate Flipper”) which focused on a local homeowner who tapped his home equity loan to finance vacant lot flips, producing $225,000 income over 18 months. But as the emergence of vacant land auctions demonstrates, not all land speculators possessed the timing instincts to liquefy as the bubble crested. As of March 2008, single-family vacant lots were selling at a median price of $13,500. That’s down from a high of $60,000. The purple line in graph 1 depicts the median price of a vacant lot over the past 25 years. The solid line running through it represents the 25-year, historical-trend line. As you can see, the vacant lot bubble is also fully deflated as prices are now below the trend line. Charlotte’s bubble demonstrated that vacant lots are much more volatile than singlefamily homes. Speculative dollars competed with local builders who scurried to accumulate building lots while still affordable. The effect of vacant lot deflation on home prices Since the bubble crested, the median home price has declined from $235,000 to $145,000. That’s a decline of $90,000. However, the median lot upon which the median home is built has dropped in value from $60,000 to $13,500 — a decline of $46,500. That means that 52 percent ($46,500-$90,000) of the median’s home’s post-bubble deflation is due to the deflated value of the land under it, as opposed to the cost of permitting, material and construction. Given that the median lot has dropped 78 percent in value, it would be easy to argue there is little room left for further downward price compression in home prices attributable to the loss of land value. The other components of value such as the brick, mortar and labor remain relatively fixed. They are not subject to the same speculative volatilities that vacant land is subject to. The price of bricks and mortar do not fall 38 percent when home prices fall 38 percent. The median home now sells for $145,000. If we back out the $13,500 that represents the value of the land the home is built on, the structure’s current market value is $131,500. An analysis of 2007 sales indicates that the typical home sold in 2007 had an average (total) square footage of 2,410 square feet. That’s $55 per square foot. With actual construction costs closer to $80 per square foot, it’s clear that today’s median price is considerably below the replacement cost. This will be selfcorrecting as builders have little incentive to compete with foreclosures and short-sales priced below cost. The aftermath The single-family home and vacant lot bubbles are fully deflated as prices have returned to their historical trend lines in Charlotte County. That does not necessarily mean we are at the bottom since the implosion of prices has created its own downward momentum that is now below the historical trend line. That creates a golden opportunity for buyers who never dreamed of purchasing a home far below the cost to build it. Brett Slattery (Keller Williams Realty) is a Realtor with a background in software engineering and analysis. His phone and blog are 941-697-8008 and www.BrettSlattery.com. Analysis and opinions presented here are based on Charlotte County public records, and are deemed reliable but not guaranteed.  Analysis Brett Slattery
.jpg) |
