Real-Estate

Forecasting Traffic and Freight Demand in order to decide on Expansion

Application Area: 

Project Details

Term: 

2019

Students: 

Sangyong Lee, Jaspreet Nayyar, Arun Nakkeeran, Rahul Mohapatra, Debayan Deb, Niladri Mukherjee

University: 

ISB

Presentation: 

Report: 

a. Problem Description

Our client, The Australian Airports Association, appointed us for forecasting the passenger and freight traffic for the month of September 2019. Based on our forecast they intend to take the decision of whether to go ahead with investing a budgeted $1.2 Billion on building new infrastructure (including runways, facilities) and recruiting staff. As per their own business estimates, they have set a threshold of a 40% increase in their YoY traffic for both passenger and freight data to decide for a go/no go on the Investment.

Forecasting house prices to optimize investments for a real estate client

Application Area: 

Project Details

Term: 

2019

Students: 

Shrisha Kashyap, Harshal Chaudhari, Prateek Agarwal, Shashank Gupta, Anuj Bairoliya, Sriram Valavala

University: 

ISB

Presentation: 

Report: 

We have forecasted time series of House Price Index to help a Real Estate company invest in a city. Our analysis recommends investing in Dallas to get 38% gain in 2 years.

 

Predicting House Prices in Taichung to Create an Online Service for the Real Estate Market

Application Area: 

Project Details

Term: 

Fall 2014

Students: 

Andi Rizki, Guerman Shion, Thimmaraju

University: 

NTHU

Presentation: 

Report: 

Today real estate market has become very popular, but the housing recovery has pushed up home prices nearly everywhere. In Taizhong, Taiwan has not been the exception, real estate market has expanded in the past couple years to the point where it is attracting interest, not only from other parts of Taiwan but also other parts of the world. An accurate prediction on the house price is important to prospective homeowners, developers, investors, appraisers, tax assessors and other real estate market participants, such as, mortgage lenders and insurers.

Forecasting Cement Prices for the Construction Industry in India

Application Area: 

Project Details

Term: 

2012 (Feb)

Students: 

Amit Tyagi, Harmanjit Singh, Mohini Jain, Nandini Chandrasekhar, Rahul Chakraborty

University: 

ISB

Presentation: 

Report: 

The aim of this project is to forecast the monthly prices of cement in India from changes in the price of crude oil. The analysis can help construction companies and other involved players manage their inventories effectively and influence their purchasing decisions.

Which Bank Should you Buy?

Application Area: 

Project Details

Term: 

2007

Students: 

Eric Grant, Yasuo Mutsuura Xiaomin Shi, Omarr Tobias, Xiaoyu Zhang

University: 

UMD

Presentation: 

Report: 

The goal of this project is to help banks that intend to grow through acquisition determine which banks to purchase. This is done by building a model to explore operational characteristics of different banks. Analysis is based on a dataset of over 1000 branches of the top five banks in the DC/MD/VA area

Comparing apartment ratings in DC vs. suburbs

Application Area: 

Project Details

Term: 

2006

Students: 

Jerome Lopez, Kenneth Ng, Sanjay Srivastava, Vishal Bhotika

University: 

UMD

Presentation: 

The goal of this project is to understand the perceived characteristics of apartments in Washington DC, and comparing them with apartments in DC's surrounding suburbs. This is done using data from ApartmentRatings.com

Determining factors that lead to a quick sell of Arlington properties

Application Area: 

Project Details

Term: 

2005

Students: 

Monisha Banerjee, Megahn Hallahan, Dave Lake, Tyler Morris, Matt Welsh

University: 

UMD

Presentation: 

Smith Realty, LLC in Arlington, VA has experienced tight cash flows, excess inventory and a limited marketing budget. The goal of this project is to build a predictive model to help use the company's marketing dollars to go after customers whose homes would be quick sells

House Investment: Profit in 30 Days or Less

Application Area: 

Project Details

Term: 

2007

Students: 

John Bradshaw, Laura Gonzalez, Richard Liao, Bharat Menghani, Michael Movesian

University: 

UMD

Presentation: 

Report: 

The project goal is to build a predictive model for predicting which houses will sell in under 30 days in the towns of Germantown and Gaithersburg, using data from the MRIS Database.

Determining Factors of a Quick Sale in Arlington's Condo Market

Application Area: 

Project Details

Term: 

2007

Students: 

Chris Frohlich, Darik Gossa, James Haas, Roger Moncarz, Jeff Robinson

University: 

UMD

Presentation: 

Report: 

The project goal is to determine the factors that lead to a ‘quick’ sale for condos in Arlington, VA, using data from from the MRIS Database.

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