Real Estate Relativity

Here are two salient take-aways from this great article detailing how Obama eviscerated previous fund-raising records

1) Strategically embrace Web 2.0 and facilitate consumer control over certain elements of an overall marketing plan

Supporters’ blogs and You Tube postings were also brought inside the campaign through the website, where the online team could help consolidate the energy and contacts generated by them.

2) Test, measure, refine, roll-out; keep what works, ditch what tanks; no “sacred cows”

[The new media team], meanwhile, was constantly testing different versions of its call-to-action pages, including requests for donations and voter registration. Did more people respond if it included video or text? Should the sign-up prompts be on the right column or in the center? Should they have a “learn more” button or direct sign-up? Once they discovered the most effective version, they replaced all the others with it. Among their lessons: Video can sometimes be a distraction rather than a help.

Trulia partnered with 1020 Placecast to provide targeted ad services.

Once users input a location they want to learn more about on Trulia, Placecast will access that data and apply it as a key component along with common demographic data points like psychographic information to provide more targeted ads.

This process makes sense especially at the zip code level (see previous posts on zip code optimization) because demo/psychographic differences exist between zip codes–even contiguous zip codes. Accordingly, if I’m looking in a zip code that trends more affluent, Trulia can now serve ads that appeal to an affluent consumer (Jaguar advertisement). Alternatively, if I’m searching in a zip code that trends more middle of the road, Trulia can now serve an ad that appeals to a bargain shopper (Toyota Corolla advertisement).

For real estate, I’d like to see a twist on this process: somehow also deduce from where a consumer searches so as to better deploy advertising resources with respect to select properties. For instance, let’s assume you’re a firm situated in a Utah ski resort community, and that you know based on previous dealings with out-of-market buyers that your to primary “feeder” markets are Chicago and Orlando, and that these primary markets are generally interested in purchasing luxury-oriented rental income properties.

It’d be a great service to be able choose which of your top properties to “enhance” that exist in a specific zip code and display the “enhanced” versions of these properties only when a consumer from either Chicago or Orlando conducts a search in the targeted zip code. Employing a scheme like this, one makes an ad buy based on a “known” marketing attribute (i.e., based on personal experience) along with hyper-targeting, which should translate into higher quality clicks to the “enhanced” properties and, thus, increase the potential ROI on those ad buys.

As always I am grateful to Owyang to lend his insight and foresight. Here’s another excellent missive on the “Intelligent Web”. In summary, he posits that machines will begin extrapolating relationships and driving recommendations for connections from the juxtapositions and nexus between “our behaviors, context, and preferences”. Sounds a bit like the semantic web. Spinning through the comments on this post brought me to the Innovation Insight blog where Guy Hagen explores MIT research related to “reality mining”, which you can find more about on the MIT Web site. And this research paper out of UC DAVIS demonstrates how the MIT Reality Mining data set was utilized in tracking behaviour via mobile phones.

Imagine an iPhone application overlayed on a real estate firm’s listing data set, where the iPhone reports back over time thousands of user’s mobile browsing habits (i.e., driving around looking at homes for sale or rent). Having such data would allow firms to target advertising, Web site promotions, and give predictive insight over their competitors with respect to fluctuating markets (e.g., patterns will emerge over time that will tell a firm which neighborhoods, etc, are capturing consumer interest, thus enabling a firm to deploy marketing and agent resources towards these locations ahead of their competition).

Assume you’re a brokerage firm with a wide distribution of properties over several zip codes. Aside from basic syndication to online aggregators, what’s another strategy to market your listings? One fee-based option that many aggregators offer is enhanced listings. Before you pay, however, ask them to prove their merit.

Assume you cover these two zip codes 28226 and 28104. According to Claritas, homes in these zip codes have very different consumer attributes (you’ll have to enter the zip codes yourself to get the results).

Armed with the demographic information, you should ask your online aggregator to give you a demographic break-down, at the zip+4 level, of it’s user audience on the search patterns and niche pages/sections of its site. For example, assume a consumer is searching an aggregator’s site in your coverage area (indicated by the consumer entering city name or 5-digit zip code as search criteria). Based on these entries, relevant properties are returned to the consumer. It’s at this moment aggregators give you an opportunity to have an enhanced listing display to this consumer.

Now it’s your turn to push back: ask for the historical demographic breakdown of the users who entered those search criteria: does the demographic base skew towards segment A (assume A is more likely to own an inexpensive American made car and have a household income below $50,000) or segment B (assume B is more likely to own an expensive foreign made car and belong to a country club)? Once you know, you will know which listings to enhance, while including appropriate imagery and content triggers that appeal to the lifestyle attributes of your targeted demographic segment.

For example, if the base skews towards segment B, perhaps you choose to only enhance listings that are 1) above $750,000, 2) close to a country club, and 3) have ample space for a boat.

Thus, you’re consolidating your advertising resources by focusing on high-gain marketing activities that give you a higher chance of getting a high quality click/lead. It’s a win for the advertiser too because they’re serving you better by giving you the opportunity to gain a high value click/lead (thus promoting retention of their services), while legitimately asking for a higher CPM or CPC for such.

What does Peter Gabriel’s new online project The Filter have to do with real estate search? (NOTE: I went through the The Filter Q&A and have to say it was eerily prescient).

What if there was a site where a consumer would 1) define the location where they want to live (via natural language, drop down, or map search), 2) answer a simple set of “lifestyle-oriented” questions, the answers to which would bump up against Claritas’ Prizm database and 3) where a real estate broker would have performed a similar zip+4 coding of their listings? When the consumer presses the “Go” button, the answers to the lifestyle questions would peg a PRIZM code to them (via session cookie or registration ID) that would relate to the same PRIZM code tagged to the properties and deliver only those matched properties to the consumer.

The benefit to the consumer is they’ve cut through gobs of listings that may not fit their lifestyle and found the ones that do. The benefit to the broker is they’ve delivered a high value service to the client. If the broker then had live chat, IM, or showing appointment booking features on each listing, there’s a higher chance of getting a conversation started and higher quality inquiry on the listing.
Right result, right time, right for the consumer.

This paper, Inferring Social Network Structure using Mobile Phone Data, explores how to use social network analysis to predict individual behavior indicators.

Privacy considerations are explored in this paper, Wherefore Art Thou R3579X? Anonymized Social Networks, Hidden Patterns, and Structural Steganography.

Here are some Videos of social network data analysis, and here is a presentation on the same. 

This paper, Social Network and Genre Emergence in Amateur Flash Multimedia, explores the concept of predicting emergent genres by mining social network data sets, which could be applied to trend-spotting.

It looks like this company is winning the Chicago real estate search engine optimization strategy and execution race. These representative results speak for themselves: 60647 homes for sale, and 60647 townhomes for sale, and 60647 condos for sale all have this website listed in Google’s top slot (at least as of the date of this post). But what really sends this site over the top in terms of customer service and Internet consumer convenience is its RSS feed.

Arguably, nothing messes with a firm’s loyalty and/or CRM strategy more than a multitude of false consumer profiles polluting a CRM database. In seeking to elevate one’s marketing engagement index, it’s often helpful to understand the demographic profile of a consumer. But if such a consumer does not self-report this, or if such data is not inferred, then firms are at the mercy of the garbage.

Interestingly, a research team claims in their research paper

The profiles users may contain fake information. We believe that our proposed algorithm can be used to identify and refine the profiles which contain bogus demographic information.

Essentially, this team analyzed web log files for search patterns and used an algorithm to predict gender or age. They claim a lift in accuracy of 30.4% on gender prediction and 50.3% on age prediction over traditional methodologies.

What makes this exciting is that, assuming futher testing bears out the team’s claims, companies like HitWise or WebTrends can incorporate this algorithm into its search pattern analsysis products. Firms can then use this core demographic information to craft more relevant landing pages, calls to actions, etc, on their websites.