Effect of Solar Panels on Home Prices Alannah Ito, Christian Herr, Justin Toguchi.

Slides:

Advertisements

Similar presentations

Regression Analysis: A statistical procedure used to find relationships among a set of variables.

Advertisements

Working with Sellers Real Living Academy Seller CMA & Pricing.

Ask a Question What are we testing? Step 1. Step 2 Research your topic. Look at the past work of others to see if this problem has already been tested.

REI ETUTOR Property Valuation. Three Approaches to Value REI eTutor Three Approaches to Value Cost Approach Income Approach Sales Comparison Approach.

Michael J. Cox Dept. of Economics CSU Sacramento.

St. Louis City Crime Analysis 2015 Homicide Prediction Presented by: Kranthi Kancharla Scott Manns Eric Rodis Kenneth Stecher Sisi Yang.

Lectures in Microeconomics-Charles W. Upton Applying Demand Functions.

Determents of Housing Prices. What & WHY Our goal was to discover the determents of rising home prices and to identify any anomies in historic housing.

© 2000 Prentice-Hall, Inc. Chap Multiple Regression Models.

Multiple Regression Models. The Multiple Regression Model The relationship between one dependent & two or more independent variables is a linear function.

Fall 2006 – Fundamentals of Business Statistics 1 Chapter 13 Introduction to Linear Regression and Correlation Analysis.

Linear Regression and Correlation Analysis

Group #4 House in P.V. vs. House in Diamond Bar Jeffery Small - Organizer Jon Lerdsuwanrut - Secretary David Lopez - Techie David Wong - Techie.

Ch. 14: The Multiple Regression Model building

Hypothesis  Foreclosure average list price will be less than for sale average list price  Foreclosures will be more concentrated in low-income areas.

Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-1 Chapter 13 Introduction to Multiple Regression Statistics for Managers.

Quantitative Demand Analysis

Spreadsheet Modeling & Decision Analysis A Practical Introduction to Management Science 5 th edition Cliff T. Ragsdale.

Does Deregulation in Electricity Lead to Lower Consumer Prices? Evidence from U.S. Utility Companies Research question: Does deregulation of electricity.

The Art Market: An Econometric Model Presentation by Jordan Pock.

Canon City, CO Real Estate Sales Forecast Model Katelyn Allenbaugh.

Linear Trend Lines Y t = b 0 + b 1 X t Where Y t is the dependent variable being forecasted X t is the independent variable being used to explain Y. In.

Regression Analysis: A statistical procedure used to find relationships among a set of variables Geography 471: Dr. Brian Klinkenberg.

 Home value will be most affected by square footage, acreage, and property type  A bigger house will be more expensive than a smaller one  A house.

Prediction of Home Selling Prices Aurora, CO By: Danyelle Canning Data gathered from zillow.com.

Linear Trend Lines = b 0 + b 1 X t Where is the dependent variable being forecasted X t is the independent variable being used to explain Y. In Linear.

The effect of different pricing strategies in the Dutch housing market Ingrid Janssen Roger Bougie Koen Pillen Istanbul, June 27th 2015 ERES 2015.

Chapter 14 Introduction to Multiple Regression

Interest in Solar Panels on Home NRECA MARKET RESEARCH SERVICES.

STA302/ week 911 Multiple Regression A multiple regression model is a model that has more than one explanatory variable in it. Some of the reasons.

CDAE Class 13 Oct. 10 Last class: 3. Individual demand curves 4. Market demand and elasticity Today: 4. Market demand and elasticity Next class:

Solar Energy By Juan Figueroa & Jake Rassman. Current Status Today, in the United States the average cost per kilowatt hour per household for electricity.

Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

Copyright © 2013, 2009, and 2007, Pearson Education, Inc. Chapter 13 Multiple Regression Section 13.2 Extending the Correlation and R-Squared for Multiple.

Regression Analysis A statistical procedure used to find relations among a set of variables.

Are the number of bedrooms and number of bathrooms significant predictors of monthly rent in the multiple regression model we estimated in class? Jill.

Linear Trend Lines = b 0 + b 1 X t Where is the dependent variable being forecasted X t is the independent variable being used to explain Y. In Linear.

Statistics for Business and Economics 8 th Edition Chapter 11 Simple Regression Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall Ch.

CDAE Class 12 Oct. 4 Last class: 3.Individual demand curves 4.Market demand and elasticities Quiz 3 Today: Result of Quiz 3 4. Market demand and.

Alternative Energy for Housing Team 2 Lindsey Jantz – Organizer/Leader Gerald Spotts – Techie Johnny Lo – Summarizer EGR 403 – 03 Fall 2007.

Zestimate it! By: Xxxxxx Xxxxxx Xxxxxxx Xxxx Xxxx Xxxxxxxxxxx.

Multiple Regression. Simple Regression in detail Y i = β o + β 1 x i + ε i Where Y => Dependent variable X => Independent variable β o => Model parameter.

Place-Making Survey Cluj Perspective. Data sources Survey of homes -MSU questionnaire - in eleven neighbourhoods in Cluj sold between

The traffic noise influence in the housing market A case study for Lisbon Sandra Vieira Gomes PhD in Civil Engineering 1 Escola Superior de Actividades.

1/15/2016Marketing Research2  How do you test the covariation between two continuous variables?  Most typically:  One independent variable  and: 

Introduction to Multiple Regression Lecture 11. The Multiple Regression Model Idea: Examine the linear relationship between 1 dependent (Y) & 2 or more.

Real Estate Sales Forecasting Regression Model of Pueblo neighborhood North Elizabeth Data sources from Pueblo County Website.

Presents:. Market Evaluation – Analyzing Property CMA Appraisal BPO.

Statistics for Managers Using Microsoft Excel, 5e © 2008 Prentice-Hall, Inc.Chap 14-1 Statistics for Managers Using Microsoft® Excel 5th Edition Chapter.

The Student Handbook to T HE A PPRAISAL OF R EAL E STATE 1 Chapter 13 The Sales Comparison Approach.

Zestimate it! Is housing picking up again? By: Xxxxxx Xxxxxx Xxxxxxx Xxxx Xxxx Xxxxxxxxxxx Xxxxx Xxxxxx.

Real Estate Sales July Mobile & Baldwin County Single Family Residential Homes Sold - Trend DecFebAprJunAugOctDecFebAprJunAugOctNovDecJanFebMarAprMayJunJul.

Tidal Barrage  Advantages  Renewable  No air pollution  No fuel costs  Produces lots of power  Disadvantages  Huge initial cost  Environmental.

Samantha Bellah Adv. Stats Final Project Real Estate Forecasting Regression Model Market: Highland Park Neighborhood Data Sources: Zillow.com E:\PuebloRESales2014Q1Q2.xlsx.

BPA CSUB Prof. Yong Choi. Midwest Distribution 1. Create scatter plot Find out whether there is a linear relationship pattern or not Easy and simple using.

BUS 308 Week 4 Problem Set Check this A+ tutorial guideline at Problem Set Week Four.

Solar Homes and “Green” Status

Chapter 14 Introduction to Multiple Regression

Chapter 13 Multiple Regression

Steps to Scientific Method

Testing Multiple Linear Restrictions: The F-test

The home improvements that add value to your property

4 REASONS ALL TUCSONANS SHOULD MAKE THE SWITCH TO SOLAR POWER

Correlation and Regression

Multiple Linear Regression Analysis

EQUATION 4.1 Relationship Between One Dependent and One Independent Variable: Simple Regression Analysis.

____________&_____________ ____________&_____________

Reducing information ASYMMETRY.

The Scientific Method.

Competitive Price Lines as of _______________________________

Presentation transcript:

Effect of Solar Panels on Home Prices Alannah Ito, Christian Herr, Justin Toguchi

 Our research question examines the housing market in Orange, CA and asks:  How does having solar panels affects a home’s sold price?  Through this, we can determine how much the home- buyer was willing to pay during the time of his purchase for a house with solar panels already installed compared to a house without solar panels. ABOUT OUR TOPIC…

 Collected from Zillow.com for homes in Orange, CA.  111 observations: 51 solar and 60 non-solar homes  Dependent variable: last sale price of house  Independent variables: age of house, number of bedrooms and bathrooms, square footage of house, lot size, if the house has solar panels, pool, other green technology, and average rating of nearby schools Data

DATA

REGRESSION RESULTS

 After the regression, we found that having solar panels does not increase prices of homes in Orange, CA.  Having solar panels actually decreased the value of the home by $17, Additionally, being assigned good schools decreased the price by $9,  The only significant variables that positively affected the price were the square footage of the house and the lot size.  Adjusted R-square is 78%, which means it is a pretty good fit for the model.  There are plenty of ways to improve, such as increasing the number of observations to the total number of homes in Orange.  Orange, CA is also a conservative city, thus the need or demand for green energy is not as appealing, thus brining the price down. CONCLUSION