Each dot represents one county in Iowa.

k-means Cluster Analysis was used (in R) to group each county into one of three clusters. For you non-quants out there, this simply means that we asked the computer to group each Iowa county into one of three groups — based on how similar that county’s voting percentage results (for all 6 top candidates) were to other counties.

The green cluster appears to be counties of strength for Ron Paul. The red cluster appears to be counties of strength for Rick Santorum, and while Mitt Romney did well in the black cluster — it’s not quite as distinct of a set as the green (Paul) and red (Santorum) clusters.

The middle row, rightmost column’s chart clearly shows the interplay between Paul and Santorum. In counties where Santorum did well (towards the top of the chart), Paul typically fared poorly. In counties where Paul did well (towards the right of the chart), Santorum typically fared poorly. Such results lend evidence to the hypothesis that supporters of Paul are not likely to be supporters of Santorum, and vice-versa.

Mitt Romney’s results present an interesting case. On the one hand, Romney (like Paul) does worse-than-average in the red (Santorum) cluster. However, Romney performs reasonably well in the black and green clusters. Such a distribution could speak to the theory that the Romney areas and the Paul areas have more in common, or that Romney has a broader appeal than either Paul or Santorum. Another theory holds that Romney is a “default” candidate for those who lack the enthusiasm about Santorum or Paul. Either way, all of the top three candidates will soon have a test in New Hampshire that will help shed more light on their long-term prognosis. Plus, we have the wildcards of a potentially re-energized Gingrich, Bachmann’s graceful exit, and the perhaps under-rated Jon Huntsman.

Global Decision will leave it to the various SuperPACs to sling the mud.

We’ve posted a number of pieces of analysis of the Irvine market on the Irvine Housing Blog, and we look forward to continue doing so on the OCHousingNews.com website. Because the new site deals with a broader geographical area than just Irvine, we have an opportunity to create city-to-city comparative analyses using advanced analytics. We look forward to further understanding the substitution effect and other trade-offs that affect consumer behavior when buying real estate.

From the above chart, we can see the long term trend of home values in the Los Angeles / Orange County region. While prices have declined considerably from peak bubble pricing, prices are still elevated over pre-bubble (year 2000) levels. As data from the second half of 2011 works its way into the Case-Shiller index values, we expect to see a continued slow decline for home values.

The second chart drills in on the mid-term: Jan 2008 to Nov 2011. In this second chart, we can see why there is still some debate in the real estate world about whether pricing has “double dipped”. Case-Shiller index values hit cyclical lows in the beginning of 2009, before homebuyer tax credits temporarily boosted demand and prices. Once the tax credits ended, in mid 2010, prices moved towards a new less-distored (but certainly still heavily policy-influenced) equilibruim. Naturally, this new equilibrium price was lower than the price when housing was subsidized by $8,000/buyer. The tax credit was ultimately a “suckers” deal, where 2010 buyers got bilked into overpaying for housing (and property taxes). In fact, most 2010 FHA 3.5%-down buyers in Los Angeles and Orange County are now underwater homeowners.

The Case-Shiller Condo index for Los Angeles Orange County shows a different result. While the index value did bottom in early 2009, its rise was more modest thereafter. That index then hit a new cyclical low in Nov 2010 and continued lower from there. Condo values are now 6% below the early 2009 temporary bottom and should continue lower in the coming months.

The above chart shows the distribution of home prices for all sales under $2M in Irvine, CA from 1/1/2010 through 7/31/2011. Irvine, CA is an expensive sub-market of an expensive region (Southern California). As a result, it is likely to feel any impact from lower conforming home limits more than most other places.

With that in mind, we’ve identified two potential price ranges that could be most impacted by the new limits. The green band represents homes that have selling prices where a 3.5% down payment represents a loan between the old limit ($729,000) and the new limit ($625,000). These properties represent 13.0% of all home sales in Irvine, CA. For the taxpayer’s sake, let’s hope that not many of the buyers in this price range are using only a 3.5% down payment. Those buyers are likely to be underwater soon as we predict continued downward drift in higher end home values in Southern California. These buyers represent one end of the spectrum.

On another point (but not the end, which would be “all cash” buyers) of the spectrum, we have buyers who put down 20%. At current Irvine, CA valuations, this is a substantial down-payment of around $170,000. For this level of royalty, we’ve used a purple band in the chart above. Using a 20% downpayment, 8.4% of sales in Irvine, CA would be impacted by the gap between the old and new conforming loan limits.

These are estimates — buyers in the green and purple bands have a few options. In order of long-term common sense for the buyer they are:

1. Pay less. Leverage seller fear that the loan limits really will reduce demand and correspondingly demand a lower price.
2. (tie) Put more down. Buy down the loan amount so that it becomes conforming.
2. (tie) Delay the purchase. The price-lowering impact from this change will be slight, but will occur over time. With an ongoing slow economy and prices above rental parity, there are no upward drivers for Irvine, CA home values.
3. Use “creative” financing. Pay the asking price but increase your monthly carrying cost for the term of the debt obligation.

Even though the higher limits don’t go into full effect until 1 Oct 2011, the delays involved in funding a loan will require that banks and brokers use the new limits as soon as possible.

Mitigating factor: long-term rates, paradoxically, plunged after the US downgrade. One can argue that it makes little sense that a downgraded asset class would be seen as safer after the downgrade, but that’s what Mr Market has said. Because rates are so low, investors will likely be interested in more non-comforming loans as the government makes its slow but necessary disengagement from being the mortgage underwriter of last resort.

One of Webster’s definitions for risk is listed as the “possibility of loss or injury.” Another definition states that risk is “the chance that an investment may lose value.” From a financial standpoint, we often think of “risk” as involving (i) uncertainty and (ii) potential loss. What’s not often said is that “risk” also involves potential unexpected gain. In fact, the heart of modern financial theory binds the concepts of risk and return into one model.

Risk, more accurately stated, is not the chance of a loss – but rather the uncertainty associated with future events. In financial terms, a risky investment is one where the future set of cash flows from that investment (including from the sale of that investment) can vary considerably.

With that in mind, let’s look at an example with respect to housing costs. Which is “riskier:” signing a one-year lease to pay $2,400 in rent per month – or buying a low-end condo with a monthly payment of $1,200? On the surface, many people will have a knee-jerk reaction to say that the high rent lease is risky. Such respondents may consider it “risky” to spend so much money on rent, or view the lower monthly payment as “safer.”

Such respondents are wrong on two counts. First, recall that risk is a concept representing variation in cash flow. The $2,400 rent, while higher, is 100% certain. Renting is risk free: the tenant has a fixed $2,400 occupancy cost and is not responsible for any unexpected maintenance or other costs. The owner-occupant, by contrast, has a fixed mortgage but is responsible for repair and maintenance – which are hard to predict on a month-to-month basis.

While repair and maintenance can be considerable, the real “risk” from owner-occupancy is that the owner-occupant also has the risk associated with the value of the underlying asset. As the timeframe from 2000-2011 has shown, home values are very volatile. They can rise and fall by 10% in a 12-month span. And when a 10% change in a home value is coupled with a 5:1 leverage (using a 20% down payment), that becomes a 50% change in owner’s equity. Thus, the owner-occupant is (perhaps unwittingly) investing in an asset that can have gyrations in equity that far exceed the average swings of the stock market.

What about long-term risks of renting?

The “risk” from renting comes at the time of lease renewal. At the end of the lease term, in most of America, the landlord has the right to request whatever rent he deems reasonable. Tenant is not obligated to accept such rent and may negotiate a lower rent or vacate the rental unit. Thus, the first uncertainty in the tenant’s cash flows (costs) comes in month 13, after the new rent is in force. And while the rental increase is uncertain, most landlords will offer a rent increase between 0 and 6% — meaning that the uncertainty is low. Over the long-term, the difference between a 0% increase and 4% increase can be significant, so the “risk” from renting occurs when renting is used on a very long-term basis. Owning also has long-term risks, but they result primarily from repair & maintenance and the change in the value of the underlying home.

Can you give a mathematical example of renting risk vs. homeowner risk?

Sure, let’s consider a 5-year span of either renting or owning with the following assumptions:

• Renter: annual rent increases range from 0% to 6%; assume a uniform distribution.
• Owner: annual repair and maintenance costs range from $0 to $3,000. Assume 0% occurs 40% of the time and 60% of the time R&M costs are uniformly distributed between $500 and $3,000.
• Owner: assume the underlying home value varies between -6% and +6% each year. Assume a uniform distribution.
• Owner: assume a 6% transaction cost to sell the home.

We can now set up a simulation model to see what the series of cash flows looks like for the owner and the renter over the next 5 years. A simple Excel file with a snippet of Visual Basic will do the trick, and we can produce the following chart from a simulation of 5,000 trials:

Conclusion: Owning a home is a high-risk proposition for many years

The red line (distribution of the cost-to-own) shows the clear risk associated with homeownership. While the renter has some uncertainty in her 5-year total cost of occupancy due to unknown lease price increases in years 2 through 5, the owner has many times more variability in her total cost-of-ownership.

Because we model the housing market as being able to either rise OR fall in value, the owner’s total cost-of-ownership could be reduced or increased – based on the unknown: the performance of home values as an asset class.

Owning a home in times of rising home values can be a strong financial gain. However, such potential upside comes with an equally large downside, where an entire down-payment can be lost in a falling market. Homeownership should thus be viewed as a financially risky activity, much as one would treat an investment in equities and other assets where past returns do not predict future performance.