Are Commercial Real Estate CRE Marketing Specialist diametric to Commercial RE Investors? If so, to what degree? In the world of blogging, social bookmarking is high on the list of talked about Web 2.0 sites. Social relationships implies networking between groups or individuals with similar or common interests. Not an unusual activity in CRE industry. Consequently, it would pay to think about the job descriptions to see where commonalities intersect for the purpose of consummating transactions. Unfortunately, this is harder to visualize then at first glance. Due to the complexity of the workplace, roles are partitioned and various duties are parceled out to several other employees so that information and resources pull together all the pieces into some form of actionable intelligence for both types of RE Professionals to use in decision making. The degree of difference may be less in different firms. A corporation’s real estate department may have buyers and sellers working together on acquisitions, disposals and mergers in the same organization. This as opposed to firms that are either strictly investment firms or brokerage companies.

In a tech sense we like visualization, so often we talk about how we can graph networks. As a starting point, we know that the marketing person charged with developing sales of real assets and investors (individuals or organizations like REITs) researching to make an acquisition, begin by searching with some notionor set of criteria for qualifying, analysis and due diligence that may lead to a contract. And no doubt, everyone’s process approach to the problem is different as is some math used in deciding to purchase, build or lease an asset.

ClubPro Property Banks^TM came about as an effort to achieve this new means of representing data where we can literally surf our way to finding a property or comparable without ever accessing a database. You may think that as the Internet blossoms more and more data and information is free. In one sense this may be true and its relative value is very low without semantic value or an ability to manipulate and use it. In another sense this is not true because in a Property Bank the data has real value the same as say one’s perception of Intellectual Property. The difference though is a property contained in the bank is structured in such a way that it now has real meaning to both machine to machine processing as well as to people searching for real estate. The same data can be taken from the property bank and passed onto the investor electronically after the sale for continued use in facilities assets, operations and personnel. People can re-purpose its use and create unified views previously impossible with today’s listing services resulting in an easier conceptual understanding, for example managing it as a component of a portfolio.

Recall I stated earlier that people network and social bookmarking sites are prevalent. Well let’s talk about a single property in a property bank. The underlying technology that empowers us is called the Resource Description Framework (RDF). Pieces of information can be linked to other information across the web. Information can refer to any identifiable thing that may not be directly available on the web such as the name of a broker. So we can identify cars, businesses, events, and property attributes. RDF properties themselves have links to precisely identify the relationships between links or linked items. Consequently, we can graph these statements because RDF is based on the idea of expressing simple statements about resources, where each statement consists of a subject, a predicate, and an object. Please note we are not talking about Cartesian graphs. Rather, we model them as nodes and arcs. So for example, a subject is a node, an object is a node and an arc for the predicate is directed from the subject node to the object node.

I bet you are having a flashback to High School English sentence diagramming days. Don’t worry, you are just going to be having fun surfing and not have to worry about the mechanics.

A property in a bank are groups of statements represented by corresponding groups of nodes and arcs. So to reflect, the additional English statements below are representative:

http://www.propertyclubpro.com/index.php has a creator whose value is John Wubbel

http://www.propertyclubpro.com/index.php has a creation-date whose value is August 16, 1999

http://www.propertyclubpro.com/index.php has a language whose value is English

RDF looks exactly like the above statement containing a subject, predicate and object. The point I am trying to get across here is how we are getting from something that is merely a data item made up of a string of characters to an item that represents knowledge.

Using references to subjects, predicates, and objects in RDF statements supports the development and use of shared vocabularies on the Web. People can discover and begin using vocabularies already used by others to describe things, reflecting a shared understanding of those concepts. Thus, with standard vocabularies such as the Dublin Core (DC) Vocabulary we strive to have unambiguous meaning to words. In other words the “creator” attribute is in the Dublin Core metadata attribute set, a widely-used set of attributes (properties) for describing information of all kinds. We are effectively saying that the relationship between the Web page (identified by http://www.propertyclubpro/index.php ) and the creator of the page (a distinct person, identified as John Wubbel ) is exactly the concept identified by another RDF statement that points to the DC on the Web http://purl.org/dc/elements/1.1/creator. Another person familiar with the DC vocabulary, or who finds out what the creator means (say by looking up its definition on the Web) will know what is meant by this relationship. In addition, based on this understanding, people can write programs to behave in accordance with that meaning when processing statements containing the predicate dc:creator.

Instead what we get back from a relational database query is a result that is a character string literal with no processable meaning. A large industrial building will be described by many attributes and features in the property bank. Within some of those RDF statements will be reference links back to the CAD Drawings that describe the physical plant itself or documents such as site plans. Now imagine 1,000 industrial properties available around the globe in the property bank. Using the Firefox Piggy-Bank extension you can now navigate from the global view down to a specific asset by filtering on types and attributes, a more natural and intuitive achievement. Finding comparable properties or creating mashups for external information discovery is now possible.

Any Diametric Differences between RE Professional parties is significantly narrowed with Property Banks. Realty firms with inventories to list on their web site or looplinks to hosted lists provided by 3rd party bulletin board listing services firms seems obsolete when consumers, investors, investment clubs have a faster direct path to your assets for sale or lease.

“Semantic” is one of those words that is rarely heard or thought about by the average web surfer, yet is very commonly used by software engineers and researchers in the areas of AI, Knowledge Engineering and more recently lingo such as the “Semantic Web” abbreviated to the “Web 2.0″. I will preface this article with a definition of the word and you will see how nice it modifies our notion of a bank.

1. of, pertaining to, or arising from the different meanings of words or other symbols: semantic change; semantic confusion.
2. of or pertaining to semantics.
3. of or relating to meaning, especially meaning in language.
4. of, relating to, or according to the science of semantics.

The Semantic Bank is the general term used for any kind of bank we define at ClubPro with a proper name. A knowledge repository we can describe whether we call it a Property Bank, Movie Bank, Library Bank, etc. The difference though when we have it contained in this form it brings with it meaning or intelligence. And without getting to technical, the information is coupled with what are known as ontogolies or vocabularies such that in certain specialty fields of knowledge ambiguity is avoided by communities that agree on definitions. At least that is a short answer without getting to in depth on the topic.

The folks at the Simile Group at MIT have invented Piggy-Bank which encapsulates this notion of a Semantic Bank within your FireFox browser. They have also made available the server version of the Semantic Bank. If you have the Piggy-Bank extension installed (See Install Guide ) you can go to our web server where we have a sample bank hosted for your browsing pleasure at: ClubPro Bank and from there you can click on categories under the heading “Data by Type” which falls into Commercial, Professional, Canadian, etc.

By having an account on this particular bank, you could add property listings found on the Web whenever or where ever Piggy-Bank can harvest from the site, what we otherwise would refer to as a Web 2.0 site. In fact, you could actually store any information not just Commercial Real Estate Listings in the bank.

A Semantic Bank allows you to:

1. persist your information remotely on a server. This is useful, for example, if you want to share data between two of your computers or to avoid losing it due to mistakes or failure.
2. share information with other people. The ability to tag resources creates a powerful serendipitous categorization (as proven by things like del.icio.us or Flickr).
3. lets you publish your information, both in the “pure” RDF form (for those who know how to make use of it) or to regular web pages, with the usual Longwell faceted browsing view of it.

And the good news is there are live banks already available:

Some Available Banks