Category Archives: 2013 Where to Buy

The Market Peak: First Quarter 2013 by Individual MLS AREAS

Posted on April 13, 2013 | Leave a comment

Here is a review (at times staggering) of the price trends for the 8 MLS areas we most frequently list and sell in. Values are compared to First Quarter 2012.

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Posted in 2013 Where to Buy, Colorado Springs Real Estate

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The Market Peak: First Quarter 2013

Posted on April 11, 2013 | Leave a comment

In April 2006, a very bored manager of a little real estate outpost on Library Lane (yes, that is the street name) decided to stick his toe back into the kiddie pool of real estate. Between reviewing contracts and making sure sellers were signing Addendum A’s, this agent went looking at the data, remembering the puzzling and vexing questions of his client, Chandra Narumanchi, who, the previous year, had the audacity to demand to know the months of inventory in his neighborhood, and what the probability of his house selling might be.

This manager made a fateful and foolish decision. He embraced his inner nerd. He began churning out data. He began to wax eloquently (and sometimes, quite opinionatedly) about the market and it’s trends. He sent out a two page SEND-ALL email written in Word with a mess of numbers. He printed it off and distributed it at a company sales meeting. He called it “The Stat Pack.”

That month, April 2006, was also a fateful month in local real estate. It was the same month that the real estate market tipped from the gonzo insanity of “buy know or be priced out forever” (a quote from former NAR lead economist David Lereah, author of 2005’s fateful “Why the Real Estate Boom Will Not Bust“). That single month, asking prices rose, sales prices mysteriously dipped, inventory soared and so did interest rates. By the end of June, there were 1200+ more listings for sale than the same time the year before. This Stat Pack thing just happened to launch at the same moment that the market tipped.

The Stat Pack has officially been retired. There are three reasons. The first is that my old (yes, the geek and the manager and the author are all one in the same. Back to first person singular) brokerage that I left three years ago insists on producing a document of the same name. The second is that documents suck and video, even bad video like mine, is better. The third is that the market has changed. We are now at a 13 year low in inventory. We are at the lowest supply of housing after first quarter in MLS history (3.5 months to sell through all of it). We are at the highest rate of sale in 6 years. And the house money of 3.6% interest rates is still out there.

Anything decent will not only be gone by Monday, but will have a bidding war take it out. Here are the cold hard facts in moving graphic form, narrated by yours truly:

 

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Green Shoots: Northwest District 11 Analysis

Posted on January 15, 2013 | Leave a comment

This slideshow requires JavaScript.

This slideshow requires JavaScript.

This slideshow requires JavaScript.

How to Read these:

Neighborhood Patterns: There are FIVE graphs, Odds of a Home Selling, Time to Sell 2012, and Buying Patterns 2012. We then supplemented these with comparisons to 2011 for both Mountain Shadows and Oak Valley Ranch. Mountain Shadows was notably impacted by the Waldo Canyon Fire with 346 homes lost to the fire. Oak Valley Ranch had the fire burn all around it’s borders, but no houses were burned. Initial guesses were that this market would be severely impacted in value. To date, that has not been the case. This has always been a seasonal market, and that was confirmed in 2012 with the highest prices generally paid June through October. The historic event to date has shown zero negative impact on pricing.

On the note of the Waldo Canyon Fire, the probability of sale in Mountain Shadows requires some deeper analysis than these visual graphs reveal. The rate of “failed-to-sell” listings is significant in Mountain Shadows and therefore the expressed probability of sale is less than 50%. But what must also be considered is that the available inventory, both today and at the time of the fire, has been historically low, and therefore a figure like “months of supply” is low, at 4.7 months at end of year, 2012.

Scattergram: something we actively look for in measuring “a good buy” is if a home is selling at near the average price, the median price, and whether or not there is a significant variance in top to bottom prices per square footage. Appraisers like neighborhoods where all the homes hug the trendline forecasting predictable values. WE LIKE neighborhoods that have prices all over the place. Many of our buyers are looking for a “good buy” and one way to measure that is to find a neighborhood with a large variance in prices. Pinon Valley is somewhat predictable in it’s pricing structure, but has multiple markets right alongside one another, with prices under $200,000, $200,000 to $225,000, $225,000 to $275,000, and then an over $300,000 market. Mountain Shadows has prices all over the place due to generations of construction and size of lot. That’s price elasticity.

These graphs also allow consumers to compare neighborhoods. Pinon Valley and Oak Valley Ranch are historically competitive, but the way pricing breaks out in Pinon Valley is more in clusters of like properties, while Oak Valley Ranch had more notable high dollar per square foot sales in 2012.

Some baselines… Probability of sale last year for the entire MLS was 63.8%. That was the highest probability since 2005. These graphs reflect mostly lower numbers, but that is because the software counts under contract properties as still “active”. In essence, these are contracts, and in certain cases, we notated what happens to months of inventory and probability of sale if you “count the contracts” that are there at the start of the year. Saying that, Pinon Valley and Oak Valley Ranch returned some of the most exceptional probabilities of sale in Colorado Springs in 2012.

If you would like any of these slides emailed to you for specific information, hit me up at Benjamin@BenjaminDay.com. Yes, we realize that they read a little small, but we’re preciously attached to our WordPress format, so, sorry.

The software used to create these graphs is from http://www.Focus1st.com and we used a date range of January 1, 2012 to January 11/14, 2013 for all of the searches, doing as many as possible on two different business days to get a competitive comparison for a single snapshot in time.

Disclaimer time: Benjamin Day composed this blog post and is solely responsible for it’s content. This information reflects data and opinion of  real estate licensee in The State of Colorado. Based on information from the Pikes Peak REALTOR Services Corp. (“RSC”), for the period January 1, 2012 through January 14, 2013 .  RSC does not guarantee or is in any way responsible for its accuracy.  Data maintained by RSC may not reflect all real estate activity in the market.

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Posted in 2013 Where to Buy, Colorado Springs Real Estate, Market Data, Mountain Shadows

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Green Shoots, 2013: Briargate Analysis

Posted on January 14, 2013 | Leave a comment

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The Geek Flag is flying high this year. This is the first analysis of the 11 MLS areas we most commonly sell in. We broke down these 11 MLS areas into a total of 95 different neighborhoods and blew them apart to see what happened in 2012.

Some baselines… Probability of sale last year was 63.8%. That was the highest probability since 2005. These graphs reflect mostly lower numbers, but that is because the software counts under contract properties as still “active”. In essence, these are contracts, and in certain cases, we notated what happens to months of inventory and probability of sale if you “count the contracts” that are there at the start of the year.

How to Read these:

Neighborhood Patterns: There are three graphs, Odds of a Home Selling, Time to Sell, and Time of Year to Sell. When applicable, we added notes. You can pause on any of the pages.

Scattergram: something we actively look for in measuring “a good buy” is if a home is selling at near the average price, the median price, and whether or not there is a significant variance in top to bottom prices per square footage. Appraisers like neighborhoods where all the homes hug the trendline forecasting predictable values. WE LIKE neighborhoods that have prices all over the place. Many of our buyer are looking for a “good buy” and one way to measure that is to find a neighborhood with a large variance in prices. Cordera for instance has an average square footage around 3500 square feet. There was a sale around $310,000 last year at that square footage and another around $530,000. The predicted price for that size home is $395,000. So one sold WAY above the trendline, and another WAY below. That’s price elasticity.

These graphs also allow consumers to compare neighborhoods. Using Cordera again as an example, the average square footage sold last year was around 250 square feet smaller than 80924 neighbor Wolf Ranch. However, Wolf Ranch homes sold on average for almost $50,000 less, and with far less variety in pricing.

If you would like any of these slides emailed to you for specific information, hit me up at Benjamin@BenjaminDay.com. Yes, we realize that they read a little small, but we’re preciously attached to our WordPress format, so, sorry.

The software used to create these graphs is from http://www.Focus1st.com and we used a date range of January 1, 2012 to January 11/14, 2013 for all of the searches, doing as many as possible on two different business days to get a competitive comparison for a single snapshot in time.

Disclaimer time: Benjamin Day composed this blog post and is solely responsible for it’s content. This information reflects data and opinion of  real estate licensee in The State of Colorado. Based on information from the Pikes Peak REALTOR Services Corp. (“RSC”), for the period January 1, 2012 through January 14, 2013 .  RSC does not guarantee or is in any way responsible for its accuracy.  Data maintained by RSC may not reflect all real estate activity in the market.

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Posted in 2013 Where to Buy, Colorado Springs Real Estate

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