What is the next big technology for real estate investors to get more leads?

The problem with the many guru selling real estate web site products is that they are only information marketers and have NO technology background.  They really only know internet marketing tactics to sell information products.  I have been trained in computer software systems and development and I take pride in staying a breast of the latest technologies and then mapping them to real estate lead generation.   The problem is that you should not use  the same internet marketing techniques  leads as information products.  Generating leads for real estate investing calls for methods that will make you stand out from the crowd and not just a copy cat.    Their sales pitches of ” done for you” ( Web 3.0, SIMS 2.o, Ibot, FreedomSoft) are no more than websites that are just another “me too”  website with some social media connectivity.  If you really want a done for you system, you have to have technology that would be classified as an Expert System.

Expert System are Key!

An expert system is software that attempts to provide an answer to a problem, or clarify uncertainties where normally one or more human experts would need to be consulted. Expert systems are most common in a specific problem domain, and is a traditional application and/or subfield of artificial intelligence. A wide variety of methods can be used to simulate the performance of the expert however common to most or all are

1) the creation of a knowledge base which uses some knowledge representation formalism to capture the Subject Matter Expert’s (SME) knowledge and

2) a process of gathering that knowledge from the SME and codifying it according to the formalism, which is called knowledge engineering.

Expert systems may or may not have learning components but a third common element is that once the system is developed it is proven by being placed in the same real world problem solving situation as the human SME, typically as an aid to human workers or a supplement to some information system.

Self service software

Self service software is a subset within the Knowledge Management software category and which contains a range of software that specializes in the way information, process rules and logic are collected, framed within an organized taxonomy, and accessed through decision support interviews. Self-service software allows people to secure answers to their inquiries and/or needs through an automated interview fashion instead of traditional search approaches.

Self Service Software Functionality

Self service software allows authors (typically subject matter experts) to readily automate the deployment of, the timeliness of, and compliance around a variety of processes with which they are involved in communicating without having to physically address the questions, needs, and solicitations of end users who are inquiring about the particular process being automated.

Self service software primarily addresses closed-loop inquiries whereby the author emulates a variety of known (finite) questions and related (known) responses on hand or required steps that must be addressed to derive and deliver a final answer or directive. Often the author using such software codifies such known processes and steps then generates (publishes) end-user facing applications which can encompass a variety of code bases and platforms.

Self service software is sometimes referred to decision support software (DSS) and even expert systems. It is typically categorized as a sub-topic within the knowledge management software category. Self service software allows individuals and companies alike to tailor and address customer support, technical support and employee support inquiries and needs in an on-demand fashion where the person with a question (need) can interface with the author’s generated application via a computer, a handheld device, a kiosk, register, or other machine type to secure their answers as if they were directly interacting (talking to) the author.

Benefits of Decision Support Systems

1.    Improves personal efficiency
2.    Expedites problem solving (speed up the progress of problems solving in an organization)
3.    Facilitates interpersonal communication
4.    Promotes learning or training
5.    Increases organizational control
6.    Generates new evidence in support of a decision
7.    Creates a competitive advantage over competition
8.    Encourages exploration and discovery on the part of the decision maker
9.    Reveals new approaches to thinking about the problem space
10.    Helps automate the managerial processes.

End user

There are two styles of user-interface design followed by expert systems. In the original style of user interaction, (as in the example below, where a backward-chaining system seeks to determine a set of restaurants to recommend), the software takes the end-user through an interactive dialog:

Q. Do you know which restaurant you want to go to?
A. No
Q. Is there any kind of food you would particularly like?
A. No
Q. Do you like spicy food?
A. No
Q. Do you usually drink wine with meals?
A. Yes
Q. When you drink wine, is it French wine?
A. Why

The system must function in the presence of partial information, since the user may choose not to respond to every question. There is no fixed control structure: Dialogs are dynamically synthesized from the “goal” of the system, the contents of the knowledge base, and the user’s responses. This approach wastes much of the user’s time, because it does not allow a priori volunteering of information that the user considers important (e.g., Northern Italian, French or Turkish cuisine, moderately priced, with large wine selection, not more than 20 minutes driving distance), and is unlikely to be acceptable to busy users – e.g., a mobile-device user who needs to obtain information as efficiently as possible. Consequently, it has fallen into disfavor. Commercially viable systems will try to optimize the user experience by presenting options for commonly requested information (based on a history of previous queries of the system) using old-fashioned technology such as forms, augmented by keyword-based search. The gathered information may be verified by a confirmation step (e.g., to recover from spelling mistakes), and now act as input to a forward-chaining engine. If confirmatory questions are asked in a subsequent phase (based on which rules are activated by the obtained information) they are more likely to be specific and relevant.

Implementing the ability, within an expert system, to learn from a stored history of its previous use, involves employing technologies considerably different from rule engines, and is considerably more challenging from a software-engineering perspective. It can, however, make the difference between commercial success and failure. A large part of the revulsion that users felt towards Microsoft’s Office Assistant was due to the extreme naivete of its rules (“It looks like you are typing a letter: would you like help?”) and a failure to adapt to the user’s level of expertise over time – e.g., a user who regularly uses features such as Styles, Outline view, Table of Contents or cross-references is unlikely to be a beginner who needs help writing a letter.

Explanation system

Another major distinction between expert systems and traditional systems is illustrated by the following answer given by the system when the user answers a question with another question, “Why”, as occurred in the above example.

The answer is:
A. I am trying to determine the type of restaurant to suggest. So far Indian is not a likely choice. It is possible that French is a likely choice. I know that if the diner is a wine drinker, and the preferred wine is French, then there is strong evidence that the restaurant choice should include French.

It is very difficult to implement a general explanation system (answering questions like “Why” and “How”) in a traditional computer program. An expert system can generate an explanation by retracing the steps of its reasoning. The response of the expert system to the question WHY is an exposure of the underlying knowledge structure. It is a rule; a set of antecedent conditions which, if true, allow the assertion of a consequent. The rule references values, and tests them against various constraints or asserts constraints onto them. This, in fact, is a significant part of the knowledge structure. There are values, which may be associated with some organizing entity. For example, the individual diner is an entity with various attributes (values) including whether they drink wine and the kind of wine. There are also rules, which associate the currently known values of some attributes with assertions that can be made about other attributes. It is the orderly processing of these rules that dictates the dialog itself.

Comparison to problem-solving systems

The principal distinction between expert systems and traditional problem solving programs is the way in which the problem related expertise is coded. In traditional applications, problem expertise is encoded in both program and data structures. In the expert system approach all of the problem related expertise is encoded mostly in data structures.

An example, related to tax advice, contrasts the traditional problem solving program with the expert system approach. In the traditional approach data structures describe the taxpayer and tax tables, while a program contains rules (encoding expert knowledge) that relate information about the taxpayer to tax table choices. In the expert system approach, the latter information is also encoded in data structures. (The collective data structures are called the knowledge base.)The program (inference engine) of an expert system is relatively independent of the problem domain (taxes) and processes the rules without regard to the problem area they describe. processing sequence and focus.

This organization has several benefits.

•    New Rules can be added to the knowledge base (or altered) without needing to rebuild the program. This allows changes to be made rapidly to a system (e.g., after it has been shipped to its customers, to accommodate very recent changes in state/federal tax codes.)

•    Rules are arguably easier for (non-programmer) domain experts to create and modify than writing code. (Commercial rule engines typically come with editors that allow rule creation/modification through a graphical user interface, which also performs actions such as consistency and redundancy checks.)

Modern rule engines allow a hybrid approach: some allow rules to be “compiled” into a form that is more efficiently machine-executable. Also for efficiency concerns, rule engines allow rules to be defined more expressively and concisely by allowing software developers to create functions in a traditional programming language such as Java, which can then be invoked from either the condition or the action of a rule. Such functions may incorporate domain-specific (but reusable) logic.

Individuals interacted with:

There are generally three individuals having an interaction with expert systems. Primary among these is the end-user; the individual who uses the system for its problem solving assistance. In the building and maintenance of the system there are two other roles: the problem domain expert who builds and supplies the knowledge base providing the domain expertise, and a knowledge engineer who assists the experts in determining the representation of their knowledge, enters this knowledge into an explanation module and who defines the inference technique required to obtain useful problem solving activity. Usually, the knowledge engineer will represent the problem solving activity in the form of rules which is referred to as a rule-based expert system. When these rules are created from the domain expertise, the knowledge base stores the rules of the expert system.
Inference rule

An understanding of the “inference rule” concept is important to understand expert systems. An inference rule is a statement that has two parts, an if clause and a then clause. This rule is what gives expert systems the ability to find solutions to diagnostic and prescriptive problems. An example of an inference rule is:

If the restaurant choice includes French, and the occasion is romantic,
Then the restaurant choice is definitely Paul Bocuse.

An expert system’s rulebase is made up of many such inference rules. They are entered as separate rules and it is the inference engine that uses them together to draw conclusions. Because each rule is a unit, rules may be deleted or added without affecting other rules (though it should affect which conclusions are reached). One advantage of inference rules over traditional programming is that inference rules use reasoning which more closely resemble human reasoning.

Thus, when a conclusion is drawn, it is possible to understand how this conclusion was reached. Furthermore, because the expert system uses knowledge in a form similar to the expert, it may be easier to retrieve this information from the expert.

Stay Tune and as always.. If it sounds to good to be true.. please investigate further.  So if you are thinking of getting SIMS 2.0 … ?  Nope..  Its not going to make you any more money than a traditional website with a call to action box.   I do not believe this is any new technology that will help you.



    • Duncan Wierman
  1. Jean
    • Duncan Wierman
  2. Jean
    • Duncan Wierman

Add Comment