With respect to new product development initiatives, he challenges us to embrace a dexterous and daring approach rather than a artless and timid one; a paradigm especially relevant in a Web 2.0 marketing environment.

Social networks will change the way real estate professionals interact with their clients. Terms like engagement, conversation, and community underpin social networks. And in “off-line” environments real estate professionals have likely “engaged” in meaningful and relevant “conversations” while building a “community” of long-term clients.
Yet many real estate professionals are reluctant to embrace social networks as […]

Research papers:
Identifying inherent privacy conflicts in social network sites
Assessing the privacy risk of sharing anonymized network data
Proposed algorithm for automatically extracting social hierarchy data from electronic communication behavior
Discusses how rumors, viruses, and ideas propagate over social social networks

There will be no online real estate revolution. No tipping point. No tidal wave of change. Just a slow rising tide that swells almost imperceptibly, carrying upon it those who seek, in time, the higher ground. citation
This viewpoint has some synergy to Yale finance professor Robert Shiller’s comments Feb 25, 2008 that lower home prices […]

When bloggers attack, has some great tips on how to respond to blogger swarm attacks. Many real estate firms are leery of bloggers and allowing their agents to blog; this post has some thought-provoking ideas on how to respond.
Interview with Jordan Behan explains how Web2.0 consumers are more informed in real estate search.
Another great post […]

Here’s a reason why Microsoft invested over $200 million in Facebook. It’s all about the data Facebook has compiled on its user base and the time this user base spends on Facebook. What’s the “veracity index” for this data? One assumes it’s higher than other data sources, since users’ incentives to enter data honestly is […]

The link-prediction problem for social networks
Link prediction and link detection in sequences of large social networks using temporal and local metrics.
Multiplicative latent factor models for description and prediction of social networks
Modeling Trust and Influence on Blogosphere using Link Polarity
Detecting invisible relevant persons in a homogeneous social network (.ppt here)

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 […]

Kevin Hillstrom, President of MineThatData has written an excellent whitepaper on conducting a multichannel forensics analysis. Why is this whitepaper an important resource to real estate firms? Because real estate firms are engaged in complex multichannel marketing endeavors. But only a handful of these firms analyze their data from a multichannel perspective.
How does a firm […]

Blog data mining
Analyzing blog comments
Modeling blogosphere trust
Privacy in ubiquitous computing
Predictive analytics
Predicting demographics based on browsing behavior

Below is some fairly recent research on motivating and behavior factors underlying social networks. The theme of this set of research is to explore how the “echo boomer” or “millennial” generation uses social media. Since real estate is an engagement-oriented Internet based service, firms should study the motivations underlying their potential recruits and future customers […]

McKinsey Web2.0 survey

Visual analysis of blog content
Web site semantic analysis
Emperical basis for social networks
Identifying brand influencers in social networks
Impact of trust in social networks
Target marketing in social networks
Correlation between LinkedIn and Facebook
Correlation between LinkedIn and Gmail, YahooMail, and HotMail