Real Estate refers to the land and the Buildings Structures and natural resources on that land. It can Include Residential Properties, Commercial Properties, Industrial Properties and land for agriculture forestry or mining. It is significant sector in the global economy with trillions of dollars invested every year. A Real Estate Price Prediction System involves building a model that can predict the sell price of a property based on various features such as Location, BHK, SQFT, and Number of Rooms, Number of Bathrooms etc. This System typically involves collecting large datasets of historical Real Estate sell prices and associated property features cleaning and preprocessing the data then, using it to train a machine learning model. The model may use various techniques and algorithms such as Linear Regression, Lasso, and Decision Tree. We will also provide the insights of the data. Using Power BI, System will be created a dashboard of our data which gives the proper analysis of the sales to our customers. Real Estate Price Prediction Project can have a practical application for buyers, seller’s real Estate Agents and Property developers who can use the model to make more informed decision.

1.
Aswin Sivam
Ravikuma
, “
Real Estate Price Prediction Using Machine Learning
”,
MSc Research Project Data Analytics, National College of Ireland
,
2017
.
2.
Gu
,
J.
,
Zhu
,
M.
and
Jiang
,
L.
(
2011
), “
Housing price forecasting based on genetic algorithm and support vector machine
”,
Expert Systems with Applications.
3.
Limsombunchai
,
V.
(
2004
), “
House price prediction: hedonic price model vs. artificial neural network
”,
New Zealand Agricultural and Resource Economics Society Conference.
4.
Selim
,
H.
(
2009
), “
Determinants of house prices in turkey: Hedonic regression versus artificial neural network
”,
Expert Systems with Applications
36
(
2
):
2843
2852
.
5.
Piazzesi
,
M.
and
Schneider
,
M.
(
2009
), “
Momentum traders in the housing market: survey evidence and a search model
”,
Technical report, National Bureau of Economic Research.
6.
Park
,
B.
and
Bae
,
J. K.
(
2015
), “
Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing data
”,
Expert Systems with Applications
42
(
6
):
2928
2934
.
7.
Wu
,
L.
and
Brynjolfsson
,
E.
(
2009
), “
The future of prediction: How google searches foreshadow housing prices and sales
”.
8.
Liaw
,
A.
,
Wiener
,
M.
et al. (
2002
), “
Classification and regression by random forest
”,
R news
2
(
3
):
18
22
.
9.
Breiman
,
L.
(
1996
), “
Bagging predictors
”,
Machine learning
24
(
2
):
123
140
.
10.
Bhagat
,
Ankit
Mohokar
and
Shreyash
Mane
, “
House Price Forecasting using Data Mining
”,
International Journal of Computer Applications
152
(
2
):
23
26
, October
2016
.
11.
Vishal
Raman
, May
2014
, “
Identifying Customer Interest in Real Estate Using Data Mining
”,
Computer Science, Economics
2014
.
12.
S.
Muthuselvan
,
K.
Somasundaram
, "
A survey of sequence patterns in data mining techniques
",
International Journal of Applied Engineering Research
Volume
10
, Issue
1
, Pages
1807
1815
2015
.
13.
Govind
Kumar
,
Ms Priyanka
Makkar
,
Dr. Yojna
Arora
, "
Real Estate Price Prediction
",
Computer Science DecisionSciRN: Predictive Analytics (Sub-Topic)
.
14.
S.
Muthuselvan
,
K.
Somasundaram
and
S.
Rajaprakash
, "
Analysis of software requirement analysis in software development process using intelligent agent with intuitionistic fuzzy analytical hierarchical process
",
International Journal of Scientific and Technology Research
, Volume
9
, Issue
2
, Pages
1844
1852
, February
2020
.
15.
Li
,
L.
and
Chu
,
K.-H.
(
2017
). Prediction of real estate price variation based on economic parameters,
Applied System Innovation (ICASI), 2017 International Conference on
,
IEEE
, pp.
87
90
.
This content is only available via PDF.
You do not currently have access to this content.