ABSTRACT

Real estate appraisal, which is the process of estimating the price for real estate properties, is crucial for both buys and sellers as the basis for negotiation and transaction. Traditionally, the repeat sales model has been widely adopted to estimate real estate price. However, it depends the design and calculation of a complex economic related index, which is challenging to estimate accurately. Today, real estate brokers provide easy access to detailed online information on real estate properties to their clients. We are interested in estimating the real estate price from these large amounts of easily accessed data. In particular, we analyze the prediction power of online house pictures, which is one of the key factors for online users to make a potential visiting decision. The development of robust computer vision algorithms makes the analysis of visual content possible. In this work, we employ a Recurrent Neural Network (RNN) to predict real estate price using the state-of-the-art visual features. The experimental results indicate that our model outperforms several of other state-of-the-art baseline algorithms in terms of both mean absolute error (MAE) and mean absolute percentage error (MAPE).

EXISTING SYSTEM:

Current research from both estate industry and academia has reached the conclusion that real estate value is closely related to property infrastructure, traffic, online user reviews and so on. Generally speaking, there are several different types of appraisal values. In particular, we are interested in the market value, which refers to the trade price in a competitive wallrasian auction setting Traditionally, both real estate industry professionals and researchers have relied on a number of factors, such as economic index, house age, history trade and neighbourhood environment and so on to estimate the price. Indeed, these factors have been proved to be related to the house price, which is quite difficult to estimate and sensitive to many different human activities. The current algorithms are 1). Regression Models and 2). Deep Walk. Regression model has been employed to analyze real estate price index. Recently, the results in Fu et al. show that sparse regularization can obtain better performance in real estate ranking. Thus, we choose to use LASSO which is an l1-constrained regression model, as one of our baseline algorithms. Deep Walk is another way of employing random walks for unsupervised feature learning of graphs. The main approach is inspired by distributed word representation learning. In using Deep Walk, we also use _-neighbourhood graph with the same settings with the graph we built for generating sequences for B-LSTM. The learned features are also fed into a LASSO model for learning the regression weights. Indeed, deep walk can be thought as a simpler version of our algorithm, where only the graph structures are employee dto learn features. Our framework can employ both the graph structure and other features, i.e. visual attributes, for building regression model.

DISADVANTAGE:

• The existing system is quite difficult to estimate and sensitive to many different human activities. There are lot of difficult works have been done with the existing systems to measure the number of factors such as economic index, house age, history trade and neighbourhood environment.
• Current research from both estate industry and academia has reached the conclusion that real estate value is closely related to property infrastructure, traffic online user Reviews and so on.

PROPOSED SYSTEM:    

We intend to employ the pictures for the task of real estate price estimation. We want to know whether visual features, which are a reflection of a real estate property, can help estimate the real estate price. Intuitively, if visual features can characterize a property in a way similar to human beings, we should be able to quantify the house features using those visual responses. Meanwhile, real estate properties are closely related to the neighbourhood. In this work, we develop algorithms which only rely on 1) the neighbour information and 2) the attributes from pictures to estimate real estate property price To preserve the local relation among properties we employ a novel approach, which employs random walks to generate house sequences. In building the random walk graph, only the locations of houses are utilized. In this way, the problem of real estate appraisal has been transformed into a sequence learning problem. Recurrent Neural Network (RNN) is particularly designed to solve sequence related problems. Recently, RNNs have been successfully applied to challenging tasks including machine translation, image captioning, and speech recognition. Inspired by the success of RNN, we deploy RNN to learn regression models on the transformed problem. The main contributions of our work are as follows: To the best of our knowledge, we are the first to quantify the impact of visual content on real estate price estimation. We attribute the possibility of our work to the newly designed computer vision algorithms, in particular Convolutional Neural Networks (CNNs). We employ random walks to generate house sequences according to the locations of each house. In this way, we are able to transform the problem into a novel sequence prediction problem, which is able to preserve the relation among houses. We employ the novel Recurrent Neural Networks (RNNs) to predict real estate properties and achieve accurate results.

ADVANTAGE:

• A picture is worth a thousand words. One advantage with images and videos is that they act like universal languages. For the given house pictures, people can easily have an overall feeling of the house, e.g. what is the overall construction style, how the neighbouring environment looks like. These high-level attributes are difficult to be quantitatively described
• Map Based Location information are most commonly effective than the viewing in raw details. The most accurate details can be viewed in simple steps
• The proposed algorithms are very effective than the existing algorithms such as LASSO and Deep Walk.

SYSTEM REQUIREMENTS
SOFTWARE REQUIREMENTS:
• Programming Language : Python
• Font End Technologies : TKInter/Web(HTML,CSS,JS)
• IDE : Jupyter/Spyder/VS Code
• Operating System : Windows 08/10

HARDWARE REQUIREMENTS:

 Processor : Core I3
 RAM Capacity : 2 GB
 Hard Disk : 250 GB
 Monitor : 15″ Color
 Mouse : 2 or 3 Button Mouse
 Key Board : Windows 08/10