Now with Commercial Property Data!

Seems these days everyone is focused on the housing front… hardly a day goes by without another data-point on prices, sales or foreclosures or any of a number of routine personal interest stories concerning the ups and downs of the housing market.

BUT… there is more to the national real estate scene than just the residential market.

Commercial real estate (CRE) is an exceedingly important sector of the economy and especially important to assess and follow during times like these when business and personal consumption activity has been so dramatically impacted by recession.

Luckily, we need not gather our own data and number crunch in order to gain oversight over the trends in the national and regional commercial property markets… MITs Center for Real Estate, Moody’s and Real Estate Analytics, LLC have done all the heavy lifting for us!

Using a “same property round-trip” methodology (very similar to the S&P/Case-Shiller repeat sale methodology used for the residential market) MIT/Moody’s/Real distill out of real property transactions the trends in pricing across several property product types both nationally and regionally.

Currently, we have added the monthly national series as well as the quarterly national and top 10 MSA series for Apartment, Office, Industrial and Retail property types.

The following Blytics present several interesting views of the MIT/Moody’s/REAL data.

Powered By G-Search!

Finally we have a solid search mechanism that gives you what you expect... quick and relevant search results.

How did we accomplish this incredible feat? … Did we use Microsoft’s new Search Server 2008? Did we integrate with Google Site Search?... Nope.

This blogger whipped up an algorithm for us that he calls “transitive meta-matching with reverse integrated lexical mapping” or TM2RILM (pronounced TM-squared-Rilm)

That’s about as much as we know about it though looking through the source we see a line that reads as follows:

PageStrength = (((rank * weight * -1/(state7 + diff) ) / 0) ^ 3.1415) + 1

We are not sure exactly what that calculation is doing but all we can think is “Look out Google!”

Now the best part of this approach s that this search mechanism is completely tunable and over the coming weeks and months we will be enhancing the search experience particularly as we add simply tremendous numbers of new data series.

Enjoy!

Radar for Housing!

The folks over at Radar Logic have graciously consented to allowing Blytic.com to re-publish their public housing data series and for that, we are grateful.

Radar Logic produces 35 home price indices that are calculated and published daily effectively creating the country’s only daily spot market for residential real estate.

Having followed these indices for some time we can tell you that they are not only accurate but they reflect with great precision and clarity the effect seasonality has on residential home pricing as well as the subtle variations in seasonality from region to region.

Currently, our filter is importing only the 25-MSA Composite index but in short order we will have the entire dataset (roughly 30 regions with 3 daily aggregates per region or roughly 90 indices) available so be sure to check back soon for some radar over your own housing market!

Below, check out the 25-MSA Composite showing all three aggragate series (1, 7 1nd 28 days) along with the year-over-year change.

More Data Is Good Data

Today, we added a new and quite large dataset!... the entire collection of Home Price Indices (HPI) provided by the Federal Housing Finance Agency (FHFA).

Using mortgage data supplied by the big government sponsored enterprises (Fannie Mae and Freddie Mac) FHFA formulates these indices using a repeat sale methodology similar to the one used to derive the S&P/Case-Shiller Home Price Indices.

Where this data excels is in coverage.

There are 684 home price indices in this dataset covering hundreds of Metropolitan Statistical Areas (MSAs) and all the census divisions some with both seasonally adjusted series.

A potentially weak point though is that this data is derived from GSE data, by definition, it only covers homes that could be purchased with a “conforming loan” (no Jumbos) and, for most series, data associated to refinance activity is included so there are points where the S&P/Case-Shiller data and FAFH don’t align well.

To at least minimally address this FHFA offers “purchase only” versions of many of the indices which, as the name implies, drops the refinance data from the source data prior to calculating the index.

To access just the purchase only data use purchase only as your search terms.

To access you areas local price index, find the MSA you are interested in and then use its official name as the search terms.

For example, for Baltimore County, MD use: Baltimore County, MD

Two New Views Added!

Today we’ve added two new views this morning that are very useful in keeping on top of the latest data as well as the latest Blytics.

The first view is called the “The Week in Charts” which simply shows you the last seven days of data series sorted by date with the latest at the top.

Our data ingestion process is running around the clock so poking in on this view from time to time will keep you both informed of the data available as well as up to date.

The second view is called “Blytics of The Moment” which gives you a constant flow of the most recent ten (10) Blytics authored in the system.

As we continue to gain author analysts (…users) peruse this view to stay on top of what is being discussed.

As always, let us know what you think!

You Can Play Data!?!?

Yes… you read right… with Blytic you can play the data.

What does it mean to play the data?... We’re not sure… we’re still trying to figure it out ourselves but here’s the concept.

In the beginning proto-humanoid analysts of all sorts probably stared up at the night sky wondering what all the shiny dots meant… surely there must be some order… some pattern… something they can glean out of all that randomness so as to impress other early hunter-gatherers with shameless baseless speculation and maybe even attract a mate.. or two!

Then… some time later… humans figured that it would be best to catalog data… keep track of things… but still… a stream of numbers may speak volumes to the rare savant but for the average… its gobbledygook!

Finally, someone (we’re not sure who) decided it would be helpful to plot out the data as it changed over time… put points on a two dimensional field... connect the points with lines and Voila!

What magic!

You see the trend!… you get sense of how the future may play out!

So getting back to the point of this post…

We think animating the data might serve to enhance the human connection made with the data under certain circumstances.

As an example, “watch” the following Blytic (activate the embedded Blytic and click the “play” button on the VCR control NOTE: if the player loads with more than 1987 initially showing in the view, grab the left timeline handle and drag it down to the left until ONLY the year 1987 is showing prior to playing... bugs bugs bugs...) showing Boston versus Phoenix home prices and U.S. Recessions:

As you can see… Boston is a very seasonal market… up-market or down, prices generally rise in the spring and decline in the winter.

Phoenix, on the other hand, has very little seasonality.

By comparing the two we aren’t drawing a statistical correlation BUT simply using Phoenix’s natural lack of seasonality as an interesting backdrop and contrast to Boston’s highly seasonal market.

NOW…

Notice that during the period of 1997 – 2000 Boston and Phoenix begin to look very similar!

In effect, the price appreciation (very hot home buying) in Boston was so significant in the late 90s that its typical seasonal pattern was almost entirely erased… winter, spring, summer or fall… prices kept rising!

Then… the 2001 “dot-com” recession arrives and BAM! … the seasonality come right back to the Boston housing market… it looks like waves again!

So, in effect, the “dot-com” recession depressed the home buying activity and restored the normal seasonal pattern.

This is an interesting finding considering that the majority of the housing boom occurred after the 2001 recession.

Watching the animation allowed you to see this action quite clearly… the waviness of the Boston data looks almost like water sloshing… calming… and the sloshing again.

Of course, you could have always figured this out by simply looking at the chart BUT we believe this connection with the dynamic movement of the data is significant.

Let us know what you think!

A Blytic Is Born!

Oh! happy day… finally… we’re live!

Welcome to Blytic.com, a new internet app that offers a unique twist on data analysis, visualization and publishing.

First though, the name… Web + Analytic… or better even still… Web + Lytic = Blytic.

So, conceptually a Blytic is a single unit of analysis (UOA) that anyone can author and then share on the Web with everyone else.

Blytics can be as simple a single data series with no analysis… just a chart… or alternatively, you can mix in a multitude of data and even provide regular analysis, updating the analyst comments with each new release of the underlying data covered in the Blytic.

For example, if you were very interested in home price trends you could author a Blytic that covers the S&P/Case-Shiller (CSI) data release which could incorporate any of the CSI series data as well as your analysis of the data.

After you are done creating your Blytic, you can publish it allowing others to view and comment on your analysis.

Once published, your Blytic will show up on the Blytic.com website in various forms BUT you also have several options for publishing your Blytic to your own website be it a blog, traditional media site or otherwise.

You can either link to the Blytic itself, link to one of several static images of the Blytic (Large, Medium, Small or Icon) OR you can embed the Blytic Player into any website or blog just like you would a video clip.

Your readers can activate the player within the context of your article, post or wiki and interact with your Blytic through the player’s rich interface.

Finally, every month when the latest S&P/Case-Shiller data has been released, Blytic.com will automatically update the data from the release and you can then provide an update of your analysis.

Sounds like fun?! … Give it a try and stay tuned to this blog for all the latest news on enhancements and new features as well as posts covering existing features and other notable events.

To start things off, take a look at the following Blytic convering the Washington DC housing market.