Real Estate web application with recommended system – AI PROJECTS

BACKGROUND

Real Estate web application with recommended system is a web application that will allow its users to view, compare, bid and make advance payments for the properties listed over the website.[2] With the help of real estate web application those issues will be easily tackled. This real estate web application is a software where property details such as available house details, schedules, address, and others are been setup by an administrator.

This Web application consists of 3 main modules:

Admin

Admin should be able to add / remove / modify any property details entered by the owner of the property for the correctness purpose.

Owner

Owner of the property will have an option to get restricted on the web application and then post their properties for users to view and buy.

User

The User should be able to Search, bid, compare and make advance payment for the property once the owner of the property has accepted the bid.

PROJECT SCOPE

This web application is an online real estate management through which individual agents or buyer can maintain their property document keeping and managing property registration and also access its information and manage all the adding, updating, deleting and some of its tasks. The Admin user can inform their agents for regarding to property and update the information regarding property and cancel the property of buyer’s choice.

LITERATURE REVIEW

According to a book “property management system seeks to advice the establishment of an appropriate framework within which to oversee property holdings to achieve the agreed short and long-term objectives of the estate owner and particularly to have regard to the purpose for which the estate is held.[8] The basic needs will be to carry out such tasks as negotiating lettings on suitable terms; initiating and negotiating rent reviews and lease renewals, overseeing physical maintenance and the enforcement of lease covenants.

Successful property management system is a demanding activity which requires relevant understanding, ability and appropriate technical and organizational skills as well as resources to successfully maintain and improve property value through to its obsolescence Property assets, which include land and buildings, are a key resource for all types of organizations, including local authorities and central governments. In the same way as other resources – human financial and information – contribute to the success of these organizations, and so does the property resource. [9]

These activities will take place within an agreed strategic framework where there is a need to be mindful of the necessity of upgrading and merging interests where possible, recognizing other opportunities for the development of potential and fulfilling the owner’s legal and social duties to the community”. Not only is a large amount of capital devoted to these assets, they can also add value to an organization through effective and often creative management.

Two of the major criticisms of inadequate management practices are the lack of a strategic approach to property management and the limited recognition of the value of these assets by property users and operational decision makers, resulting in potential asset becoming a major liability [8].But many organizations, internally and externally, have responded to the challenges and introduced a number of measures in order to improve their management practices related to operational property.

The purpose of creating this Real Estate Web Application is to outcast the discrepancies in hundreds of such existing systems on the World Wide Web. One of the basic problems with the existing systems is the non-interactive environment they provide to the users. Most of the applications involved in Real Estate business use some web template to put the content specific to their company and make it communicate with the database to search the listings. These templates simply use basic web controls to do this task making the web page non-interactive. On the other hand, the motive of this Real Estate Web Application is to allow the user to play with the search tool and create different combinatorial search criterion to perform exhaustive search.

Another problem in such applications designed so far is the use of traditional user interfaces which make continuous post backs to the server; each post back makes a call to the server, gets the response and then refreshes the entire web form to display the result. This scenario adds an extra trade off causing a delay in displaying the results. Making such applications AJAX enabled gets rid of these unnecessary delays letting the user to perform exhaustive search. The users of this application can easily feel the difference between the Ajax empowered user interfaces vs. traditional user interfaces.

Scrutinizing the features of the existing systems reveals another problem, when the user tries to save some property listings; the user is forced to login as a buyer/user of this website. Once the user logs-in, he can then view this saved list. In contrast, this application uses the session state to maintain the list of saved property listings rather than making the user register first. As soon as the user performs a search, a new session is created and then lets the user select a listing, drag and drop it to the “Saved Search” tool. This tool then keeps track of this list until the session expires.

PROBLEM DEFINITION

Property management system for this organization uses traditional method of keeping records of the client’s files. [4] This manual record keeping in the organization has been characterized by a lot of problems, such as:

  1. Lack of skill in interpretation of reports from the activities of the organization.
  2. Data losses: loss of data perhaps would happen if all information only kept inside paper on.
  3. Data redundancies: abundant and repetition data also perhaps will happen.
  4. No database to store information: by using manual system, loss of data perhaps will happen.

FUNCTIONAL REQUIREMENTS

Our proposed system of (REWA) functional requirements are as follows:

Registration

User can register their self in the system. Admin will verify their profile to work properly.[3] Once a user is registered with the system He/she can register his/her property to property management system.

Admin will view the property and verify that property only if that would be real with reasonable price.

Validation

Validation is very important in the system. Invalid data can corrupt the valid data. So, we need to apply validation in each module. Validation would ensure the safety and security of data.

Client Record

Client data should be secure. We should take care of client’s data privacy in our mind. The client is the basic unit of our system. Client data and record would help us to provide them better information.

Add Property

This function allows the admin and client to add the property details. But only admin can verify the property details. Without verification property details would not reflect the system.

Block Diagram

CONTENT BASED FILTERING RECOMMENDATION ALGORITHM

The steps in recommending products or contents to the user in content based filtering areas follows:

  1. Identify the factors which describe and differentiate the products and the factors which might be influential in whether a user would buy the product or not,[9]
  2. Represent all the products in terms of those factors or descriptors or attributes,
  3. Create a tuple or number vector for each product that represents the strength of each factors for the product,
  4. Now start to look at the users and their history and create a user profile based on their history. It will have the same number of factors and their strength would indicate how much influenced the user is towards that factor,
  5. Recommend the user those products that are nearest to them in terms of those factors.

Content based filtering

This is one of the simple approach of recommending products or contents to the user. [6] The idea here is that if a user indicates (s) he likes a product by clicking, or by searching or browsing it means (s) he has the high possibility that they would buy the product. Now our approach for recommending the products would be to recommend the product to the user which has similar attributes or descriptive characteristics of the product like it would be brand of the product, color of product, and size of product and so on. Normally content based filtering is famous with text documents, articles and more.

Another common approach when designing recommender systems is content-based filtering. [8] Content-based filtering methods are based on a description of the item and a profile of the user’s preferences. These methods are best suited to situations where there is known data on an item (name, location, description, etc.), but not on the user. Content-based recommenders treat recommendation as a user-specific classification problem and learn a classifier for the user’s likes and dislikes based on product features.

In this system, keywords are used to describe the items and a user profile is built to indicate the type of item this user likes. In other words, these algorithms try to recommend items that are similar to those that a user liked in the past, or is examining in the present. It does not rely on a user sign-in mechanism to generate this often temporary profile. To create a user profile, the system mostly focuses on two types of information:

  1. A model of the user’s preference.
  2. A history of the user’s interaction with the recommender system.

Basically, these methods use an item profile (i.e., a set of discrete attributes and features) characterizing the item within the system. To abstract the features of the items in the system, an item presentation algorithm is applied. A widely used algorithm is the tf–idf representation (also called vector space representation). The system creates a content-based profile of users based on a weighted vector of item features.

Cosine similarity

We all are familiar with vectors: they can be 2D, 3D or whatever-D. Let’s think in 2D for a moment, because it’s easier to picture in our mind, and let’s refresh the concept of dot product first. The dot product between two vectors is equal to the projection of one ofthem on the other. Therefore, the dot product between two identical vectors (i.e. with identical components) is equal to their squared module, while if the two are perpendicular (i.e. they do not share any directions), the dot product is zero. Generally, for n-dimensional vectors, the dot product can be calculated as shown below.

Dot product

The dot product is important when defining the similarity, as it is directly connected to it. The definition of similarity between two vectors u and v is, in fact, the ratio between their dot product and the product of their magnitudes.

WHY CONTENT-BASED FILTERING?

  • Collaborative filtering may be the state of the art when it comes to machine learning and recommender systems, but content-based filtering still has a number of advantages, especially in certain circumstances.
  • Results tend to be highly relevant.
  • Because content-based recommendations rely on characteristics of objects themselves, they are likely to be highly relevant to a user’s interests. This makes them especially valuable for organizations with massive libraries of a single type of content (think subscription and streaming media services).
  • Recommendations are transparent.
  • Content-based filtering avoids the cold-start problem that often bedevils collaborative-filtering techniques. While the system still needs some initial inputs from users to start making recommendations, the quality of those early recommendations is likely to be much higher than with a system that only becomes robust after millions of data points have been added and correlated.
  • New items can be recommended immediately.
  • Related to the cold-start problem, another issue with collaborative-filtering is that new objects added to the library will have few (if any) interactions, which means they won’t be recommended very often.

CONCLUSION

After the completion of project, our system abled to display the result i.e. property like houses etc. in image form as per the user desired. For to display user desired results we use recommendation system using content based algorithm (Cosine Similarity) which makes easy to see the desired property to see for the customer. As, our frontend part is simple and easy to use. Using Django framework and html, CSS, JS backend and frontend part is developed. Using my SQL database is created.

Hence, Real Estate web application with recommendation system using content based filtering (Cosine Similarity Algorithm) works successfully.

REFERENCES

  • Retrieved from Real Estate: www.real-estate.com/management-system.nic.in
  • Retrieved from project management: www.project-management.basics.com
  • Retrieved from slideshare: https://www.slideshare.net/divyeshsarkar/se
  • Retrieved from krex k stete edu: https://krex.k-state.edu/dspace/handle/2097/957
  • Retrieved from faisalsikder: https://faisalsikder.wordpress.com/2009/12/19/software-development-life-cyclesdlc-incremental-model/
  • https://towardsdatascience.com/how-to-build-from-scratch-a-content-based-Movie-recommender-with-natural-language-processing-25ad400eb243
  • https://en.wikipedia.org/wiki/Recommender_system#Content-based_filtering

BOOKS

  • Orvik, S. a. (n.d.). The object Oriented Approach Concepts, System Development and Modelling with UML.
  • Roger Pressman, M. H. (n.d.). Software Engineering: A Practioner’s Approach . international edition.

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