1. Introduction
The assessed coursework for BME 0011 – Econometrics is a 3,000 word research report. This is intended to allow you to fulfil the learning outcomes for the module that are included in the course outline. Your report should cover material from throughout the module and demonstrate the skills you have learnt.
2. Report Topic
The report topic is based around investigating the ability of google trends data to forecast economic variables. For example does an increase in searches for DIY mean we can expect an increase in the levels of retail sales in this area. The focus is on using the econometric techniques we have seen on the module in order to do this.
The search term and variable which you investigate can be chosen by you, but should be confirmed with the module leader, for illustration you might consider:
– The relationship between searches for “mortgage deals” on the number of house sales in the UK
– The relationship between searches for “winter coats” and the timing of retail sales for clothing in the UK
– The relationship between searches for “apple” and electronics sales in Italy
You should use the econometric techniques learnt in this module to identify whether the search term helps improve a short run forecast of the variable you are considering. You will also need to identify how other techniques might be used to improve your report and whether the results are in line with your expectations.
Therefore the topic of the research report is:
Report on the ability of Google Trends data to accurately forecast an economic variable, such as a component of the Retail Sales Index.
3. Organisation of the report
You should consider how you want to structure your report; it will have a common theme running through it but the different parts of the report will address different aspects of the work you have completed. A Suggested structure is included below, however feel free to deviate from this as you feel is appropriate for your work.
- Introduction
- Literature Review
- Econometric models/methodology
- Data summary
- Results and analysis
- Issues/extensions to the econometric methodology
- Conclusions
When assessing your work we will be looking at a number of factors including:
- The clarity of the explanation of the analysis and the appropriateness of the arguments made
- How well you explain the methodology used
- The identification of appropriate data sources
- The depth of your understanding of how more advanced econometric techniques may, or may not, be applied to the subject of your report
- The validity of the conclusions reached
4. Further Reading
The papers below are core references for this project as they provides information about previous work in this area. Note that not all of the material in these papers will be of use but they will be used as a core reference for the briefing for the project to be delivered during one of the course classes.
Ayoubkhani, D. (2012) ‘An investigation into using google trend as an administrative data source in ONS’, United Nations Economic Commission for Europe, WP4.
Chamberlin G (2010) ‘Googling the Present’, Economic and Labour Market Review (Dec 2010), available at: http://www.statistics.gov.uk/cci/article.asp?ID=2621&Pos=5&ColRank=1&Rank=1
Choi H and Varian H (2009) ‘Predicting the present with Google Trends’, Google Inc, available at http://static.googleusercontent.com/external_content/untrusted_dlcp/www.google.com/en//googlebl ogs/pdfs/google_predicting_the_present.pdf .
In addition you should consult the textbooks, articles etc recommended throughout the course and any other sources which you believe will be useful to your particular event study.
5. Referencing and Plagiarism
The referencing style for this report is the APA 6th style which is the University’s recommended style of referencing. Further information on this style can be found at:
http://www.hud.ac.uk/library/finding-info/apa-referencing/
Checks will be carried out for plagiarism, and if identified the penalties can be severe. Plagiarism covers the inclusion of work from other students, websites, books, journal articles etc. Guidance on plagiarism can be found from:
http://www.hud.ac.uk/students/unilife/studentnews/areyoucheating.php
And a more formal definition of plagiarism is included on the University’s regulations on academic misconduct which can be found at:
6. Presentation Style
- The word limit for the report is 3,000 words, with a 10% tolerance, after which marks will be deducted
- The word limit does not include tables, graphs and references
- Please note than any graphs or tables included must be referred to in the text. You will receive no credit for output which is not important to the discussion in the text of your report
- Quotes from sources will count towards the word limit
- The presentation must be in the form of an electronic document
- Please use a 12 point font size.
- Try to make sure that you spell check your work before submission
- It is fine to use headings in your report to break up the work into sections
7. Submission
The deadline for submission of the report is 23:59 on Friday 29th April 2016, penalties will apply for late submissions.
All submissions will be electronic via the Turnitin assignment set up on the course Unilearn page.
You must also submit the data you have used in order to compile your report, as a sample of data will also be checked to ensure authenticity.
Paper submissions will not be accepted.
Solution
Google Trends Data Analysis: The relationship between Searches for “Housing Price” on the Number of House Sales in the UK
Introduction
In the United Kingdom, the real estate market for the consumers has focused their attention on hunting houses digitally rather than the traditional method of visiting the home itself and firsthand information. The easy access to search engines such as Google and the availability of the internet in the United Kingdom has made the house-hunting process more digital than ever. Many people in the United Kingdom are now using search engines to look for more information about the real estate market to support their decision in finding a better home (Bennohr & Oestmann, 2014). According to a study on the digital house hunt, consumers are going online at a rapid pace to look for more information on house prices and other variables to support their decision on purchasing a new home (Fernandez, Mooney, & Johnsmeyer, 2014). According to this research study, consumers use Google search engine to read reviews, look up specific house brands and even search on the go using their smartphones and other digital gadgets on housing trends.
The use of search engines to find information about the real estate market to support consumer decision-making process has encouraged the idea that shopping is no longer about showing up on the actual store. One can now get the same information through the internet in the comfort of their home. On the other hand, real estate developers and agencies are also closely monitoring consumer behavior and always adjusting their marketing methods using user search trends. Because the actual estate market in the United Kingdom is increasingly becoming competitive, the sellers in the market have to monitor carefully consumer behavior and change based on the customer expectations. This is because real estate professionals know that their clients are well connected and informed in the expense of the internet.
According to a real estate research study, nine out of ten home buyers rely on the Internet as their primary source, while 52% of home buyers turn to the web as their first source of information (Fernandez, Mooney, & Johnsmeyer, 2014). According to the research study, 22% of searches made on Google were related to real estate. Furthermore, research study suggests that a fifth of searches made on google.com were done price related and 120% more Google search on properties were drawn between the years 2011 and 2012 in the United Kingdom market ( Google, 2013). This statistical analysis shows that consumers rely more on search engine trends on real estate market more than the contacting every real estate agencies for such information. The research study also demonstrates that consumers trust search engines to provide accurate information on house prices rather than receiving directly from real estate professional agencies.
The increase search engines trends and the statistical analysis proves that there is a stable relationship between search engines trend information and the sales volume of houses in the United Kingdom. An increase in the search engine about house prices can influence the seller’s decision to adjust their prices to meet consumer expectations and also increase sales volume. If users consider the price levels to be favorable, this is likely to increase the sales volume of houses in the real estate market in the United Kingdom (Bennohr & Oestmann, 2014). The rapidly changing and digitally driven media environment is making real estate tougher to operate as a seller as consumers are increasing aware of the information available in the industry and how they work. Alternatively, the real estate professionals can use search engine trends to understand digital marketing strategies to use apply to attract consumers.
Literature review
The real estate market today have become a highly sophisticated market for sellers due to the increase in consumer awareness of the market. The media driven environment has enabled consumers to become more aware of their surroundings and approach situations different. Research studies prove that online search engines such as Google have empowered the consumers as consumers can now do their homework on house prices before engaging with the seller directly (Fernandez, Mooney, & Johnsmeyer, 2014). Users are now capable of bouncing back and forth from house to house at their speed in a multichannel marketplace. This study demonstrates that Google search engine trends have provided a credible platform for consumers to understand home prices trends and determine the appropriate price tag that suits their needs. Also, users can judge the environment using the home prices to determine their position in the market and also access further information about the target house.
A research study on digital house hunting found that 90% of home buyers use Google search engines during their home buying process (Ayoubkhani, 2012). This study suggests that most home buyers determine their price rates through analyzing Google Trends on house prices. This way they can understand the real estate market and also select what type of home they would like to buy. The research further suggests that the number of related estates-related searches on google.com has grown by 153% since 2010 to 2014 ( Google, 2013). The research study found that buyers consider Google search engines as satisfactory due to the nature of the presentation of the information. The research further suggests that shoppers take real action on real estate before an average of 11 searches made on Google ( Google, 2013). Also, the research study suggests that 9% of home buyers who use Google search engines to determine house prices tend to take action on a real estate brand website than those who do not use search engines.
According to these studies, an increase in some search engines on house prices might likely influence the sales volume of homes in the United Kingdom (Choi & Varian, 2009). This is because home buyers use house prices to justify their actions based on search engine data trends. Research studies assume that consumers use search engines mainly to compare home prices to support their decision-making process. This is because prices play a significant role according to many home buyers.
Many home buyers claim that considering the price of the house is an important factor because it shows the customer whether they can buy the house or not. Researchers believe that consumers use search engines to determine house prices because they do not discriminate or provide inaccurate information (Choi & Varian, 2009). On another hand, a real estate agent or professional can provide inaccurate data to make a sale. Therefore, it is important for the buyers to consider search engines in determining the house price rather than an agent.
According to the study on the digital house hunt, home buyers use search engines in investigating about house prices mainly because of understanding how homes are valued or to compare prices for different rooms (Choi & Varian, 2009). The research studies further claim that buyers who use search engines to determine home prices take time to make decisions as compared to those who do not. However, despite their lengthy decision-making process, the research study further claims that internet based home buyers engage with agents earlier in the process. According to this research study, taking extended analysis windows yields many opportunities to reach home shoppers during the same period.
Based on the research study, Google Trends searches on house price by home buyers provides a better platform to understand the real estate market in the United Kingdom and also help in the prediction of the future for the sales of houses in the United Kingdom’s real estate market (Lynn & Brynjolfsson, 2009). Google searches done on home prices can contribute to providing a track record into what the consumers are willing to pay and also assist in determining when the market for houses is at its highest. Real estate can determine when the sales volume for homes are expected to increase and when they are scheduled to drop. One can determine when the demand market for houses is high and during what period the application market is at its highest. Real estate is also able to use Google search trends on home prices to understand consumer preferences and taste both on the current basis (Lynn & Brynjolfsson, 2009).
Based on the research studies on Google searches on house prices by homebuyers, real estate marketers can use data obtained from the search trends and determine what consumers are willing to pay and also able to know what other factors consumers look at when identifying potential house purchases. According to research studies, an increase in Google searches for house prices means that many home buyers are becoming cost cautious which indicates a decrease in some sales of homes (Ayoubkhani, 2012).
According to these studies, when consumers are aware of the house prices before meeting with real estate agency or seller, the buyer is likely to take time in buying the house (Fernandez, Mooney, & Johnsmeyer, 2014). This is because when the consumer is aware of home prices, he or she is likely to negotiate for lower prices resulting in a reduction in sales. When the search trends on home price are low, the sellers can make a sell because buyers are more likely to buy the house at the cost of the real estate agent or professional. Therefore, when the trend in home price searches increases, the number of house sales are likely to fall because buyers are aware and cost cautious (Fernandez, Mooney, & Johnsmeyer, 2014).
The research studies conducted on Google search trends demonstrate that Google presents an excellent platform for analyzing consumer search trends on house prices. Real estate professional can use the data acquire through Google’s user search trends on home prices to determine future sales volume for homes in the housing market (Bennohr & Oestmann, 2014). If buyers have enough information on home prices in the housing market, there are likely to become selective and take a long time before making the decision to purchase a home. This reduces the number of homes sold during that particular period. Therefore, small search trends indicate a reduction in buyer awareness of the house prices which increases the possibility of the purchaser buying a home.
The research studies further suggest that customers who use search engines to determine house prices are more likely to take a period of 60 days to 120 days before they take action (Fernandez, Mooney, & Johnsmeyer, 2014). This reduces the number of home sales during that particular period. The research study further demonstrates that consumers who used Google search engine to determine home prices and take a period of 120 days to act were mainly 40% in the United Kingdom. In comparison, the number dropped to 17% for those who take 60 days to determine house price through Google search. The number is rapidly reduced to 3% for those who take two weeks (Fernandez, Mooney, & Johnsmeyer, 2014) This demonstrates that buyers who are well informed about home prices before purchase are more likely to take a long time before making a purchase. This research study proves the credibility and ability of Google search trends in helping to forecast house sales volume and determine consumer purchasing behavior during a particular time frame. Real estate marketers and professionals can use these patterns to determine high demand seasons and low demand seasons on house purchases.
Methodology
The research methodology for this study is mainly quantitative research where a systematic investigation of the relationship between searches on homes prices and the number of home sales in the United Kingdom was done, and the data was obtained through statistical analysis of the findings from the secondary sources used. The sources used in this study includes consumer market tends and real estate in Google search engine and forecasting existing home sales with Google search engine.
Statistical analysis
Research to action lags data analysis
Summary of Data
In the statistical data, 49% of home buyers considered search engines as their first option in determining house prices. Also, only 24% of the home buyers within the United Kingdom considered applying to real estate agencies on information about house prices as their first option. Only 31% of the home buyer’s population in the United Kingdom found news websites as their first choice in determining house prices in the real estate. However, the 40% of the home buyers used search engines the entire time to determine house prices, as 29% of house buyers in the United Kingdom relied on news websites throughout for information on house prices. The research data also showed that 37% of the home buyers used maps throughput as their second option to determine house prices. However, the rate increased to 44% when home buyers considered applying to real estate agencies throughout in determining house prices.
According to research to action lag data, home buyers who took 120 days on the search engine to determine house prices had a 40% response delay. Customers who took 60 days in researching on home prices through search engines made had 24% response lag. However, the delay of reply for buyers who took three weeks was extremely low of about 12%. For the consumers who took only one day to research using search engines on house prices were 24% increase. In this analysis, the data were based on the frequency a single buyer would take to gather enough information on home prices and the length of the decision to purchase a house.
Conclusion
Based on the research studies analyzed in this study, there is a significant relationship between Google searches on housing prices and the number of sales on houses in the United Kingdom. Buyers can increase their knowledge through the search engine such as Google to determine home prices before they visit their agent. An increase in consumer awareness through searches on real estate makes the buyer more cautious in their decision to make quite purchase without comparing other house prices (Lynn & Brynjolfsson, 2009). This reduces the chance of the purchaser to buy homes in the make thereby reducing their sales volume in the market.
Google search trends on house prices by home buyers can be used as a forecasting tool in for real estate professionals and other properties markers. The search patterns can also be used in analyzing consumer purchase behavior on houses which is a factor in determining the future sales level of homes in the United Kingdom. When buyers are unaware about the house prices, they are likely to make quick decisions on home purchases which increase the sales volume for the housing industry in the United Kingdom. A real estate profession can use these trends to Google search to forecast on demand levels of houses in the housing within the United Kingdom and be able to estimate peak and off-peak seasons on houses (Choi & Varian, 2009).
The high number of consumers using Google search engines to analyze home prices proves that Google search is an essential tool for real estate markets who rely on data to identify user preferences and taste as they change over time. Marketers can use these search trends to determine which marketing strategy to use to convince potential buyers of buying a house during that period. The patterns provide reliable information since it does not discriminate any customer. It also provides precise information about the kind of searches the customers used to determine the prices of houses in the market.
Google search trends accumulate data over a period through analyzing buyer searches on home prices. These search patterns affect the number of homes sold by providing more information to the consumer that can be perceived as expensive. The availability of information through search engines such as Google creates a barrier to the number of houses sold to consumers. The number of sales a real estate marketer can make sales at this point is limited because the buyer is more aware of the housing price is the leverage for the marketer (Choi & Varian, 2009). When the buyer has the knowledge on home prices, they are likely to negotiate for a lower price which reduces the likelihood for the consumer to accept an amount he or she would have rather chosen when they the buyer did not have the information.
The level of Google search trends on house prices can affect the consumer’s decision to make a quick purchase. This reduces the possibility of the purchaser to make a purchase for any room after an extended period. Real estate and marketers can use the trend data obtained to evaluate the condition of the housing industry be frequently analyzing the type of search patterns the consumers are looking. The number of houses sold can be determined by the amount of information on the internet that a buyer can access.
The trend analysis can also be a significant barrier to the development of the industry because the information provided are of general nature rather than addressing a particular environment. Real estate marketers and professional can use Google search trend data to determine the allocation process of houses. When the Google search trends show that the level of buyer awareness is quite, the demand for homes are small and buyers offer low prices (Bennohr & Oestmann, 2014). However, the rapid growth in knowledge forces the prices of the house to drop significantly. Marketers can adjust their prices levels to suit better the buyer’s needs. Therefore, Google search trends can influence the number of homes sold in the market because a change in searches can either reduce the number of houses sold and cause an increase in price as a result or cause an increase since marketers can offer good prices at the appropriate time.
References
Google. (2013). The Digital House Hunt: Consumer and Market Trends in Real Estate. The National Association of Realtors, 28.
Ayoubkhani, D. (2012). An Investigation Into Using Google Trend as an administrative data source in ONS. United Nations Economic commission for Europe.
Bennohr, L., & Oestmann, M. ( 2014). DETERMINANTS OF HOUSE PRICE DYNAMICS. WHAT CAN WE LEARN FROM SEARCH ENGINE DATA? economics journal, October.
Fernandez, J., Mooney, A., & Johnsmeyer, B. (2014). House Hunting Season: 6 KEY TRENDS THAT SEARCH REVEALS. Think with Google, 10.
G, C. (2010). Googling the present, Economic and Labor Market review. Retrieved from Statistics: https://www.ons.gov.uk/
H, C., & H, V. (2009). Predicting the Present Google Trends. Economic Journal.
Humphrey, B. D. (2010). Forecasting Existing Home Sales using Google Search Engine Queries. Economics Journal.
Wu, L., & Brynjolfsson, E. (2009). The Future of Prediction: How Google Searches Foreshadow Housing Prices and Sales. Economics journal, 24.
