The information technology, which is inescapably penetrating all facets of industry and our lives, is propelling the real estate industry into unknown territory. The number of websites providing access to information about property and its environs is steadily increasing. By automating various processes, cost-effective websites offer high quality services. Despite on-line access, one has to analyze a seemingly infinite variety of information to find desired houses or properties. Most buyers, who use the web to investigate real estate, login and re-login to many websites to keep track of houses, which is rather time consuming. Sometimes properties of interest have sold even though a buyer is still waiting for the price to come down. Moreover, real estate websites typically provide large lists of houses without taking the preferences of a particular customer into account. Some websites use proprietary protocols and formats to store and publish house listing data. While these formats are easy to read, there is no common format and thus making it difficult for web developers to consolidate data from different sources. The goal of our research is to design a flexible and extensible architecture for a real estate engine to be able to draw data from different real estate sources effectively. In addition, it is important to design this engine to display properties based on user preferences, rather than merely providing a list of all properties currently available. In this thesis, we present the design and implementation of the components of our “Houses In Reach” real estate engine, which addresses the problems mentioned above. The main components of this web engine include an architectural model, search engines to look for pertinent information on the web based on user preferences, and a visualization engine to display houses on Google maps. The thesis concludes with a discussion of our experience building these components.