(20-30 hrs)
Classes consist of interactive, online video conferences. Office hours are one-on-one online video conferences. The capstone project is presented online – students are invited to (but not required to) attend the project presentations of their classmates.
Meet your instructors.
Nelson is the CEO of PropertyQuants Pte. Ltd., a PropTech startup bringing quantitative methods to global real estate. He has a PhD in Decision Sciences from INSEAD, is a CFA Charterholder, and completed his undergraduate work at Columbia University, double majoring in Economics and Mathematics-Statistics. Nelson holds adjunct faculty instructor roles at the Singapore Management University and National University of Singapore's Asian Institute of Digital Finance.
He has published papers in Management Science, Decision Support Systems, and Decision Analysis, one of which received a special recognition award. Nelson started his career as a trader/researcher at R G Niederhoffer Capital Management, an award-winning US hedge fund deploying systematic data-driven medium and low frequency strategies to global markets, and also spent significant time as lead trader at KCG, a leading global high frequency algorithmic trading firm.
He was also a Quantitative Macro Strategist at GIC and Managing Director at a proprietary trading firm (Acceletrade Technologies). Nelson has been investing in international residential real estate in a personal capacity for 10 years, and has a deep interest in bringing more systematic, quantitative, and data-driven approaches to real estate practice.
Xingzhi is CTO of PropertyQuants and has a PhD in Statistical Physics from the National University of Singapore (NUS) and a B.S. in Computer Science from Peking University, with papers published in Physical Review Letters and elsewhere.
He was a postdoctoral research fellow at the Santa Fe Institute and NUS before moving to quantitative trading, where he has 5 years of experience as a researcher, trader, and quantitative developer.
Xingzhi enjoys architecting and developing software and frameworks for systematic and automated research. He’s also developed mobile apps and several different websites in his free time, one of which focused on tracking SGX-listed REITs, and another which analyzed which properties were best to buy or rent for parents in Singapore looking to maximize primary school admission priority for their children. He’s currently excited about building the PropertyQuants platform enabling quantitative and systematic approaches to be applied to real estate investing globally.
Participants with a basic foundation in mathematics / statistics at a high school level (GCE ‘A’ level, International Baccalaurate, or equivalent) or higher.
Participants without a background in Python, Pandas, and Sci-kit Learn are required to participate in the bootcamp prior to the course.
The topics we cover are novel and constitute an extension to typical data science courses. Experienced data scientists will gain significant value from participating in all sections of the program.
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by Ekaterina Butyugina
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Organizations are constantly seeking new ways of leveraging data to guide strategic decision-making and increase returns on investment (ROI). One of the areas of investment that benefits from a data-driven approach is real estate. To explore the impact data science can have on this form of investment, we partnered with Haystacks , a real estate investment strategy company. This blog post dives into our innovative approach, specifically focusing on the interplay between investor portfolios and Points of Interest (POIs) in real estate areas. We also detail the obstacles we encountered and the crucial insights acquired along the journey.
The task at hand was to explore how POIs affect a real estate portfolio, specifically utilizing a non time-series approach to automate correlation tests and visualizing potential correlation of POIs with real estate returns. POIs are of significant interest in real estate due to several reasons. Firstly, POIs provide insights into the available amenities and resources in a neighborhood. For potential homeowners or investors, knowing the proximity of schools, hospitals, restaurants, parks, and other essential facilities can greatly influence their decision-making process. Access to quality education, healthcare, and recreational areas are often important factors that contribute to the desirability and value of a property. Additionally, POIs can provide a sense of the overall infrastructure and development in an area, indicating its potential for future growth and investment opportunities. By analyzing the relationship between POIs and real estate portfolios, investors can gain valuable insights into the attractiveness and livability of a location, helping them make informed investment decisions.
Our journey commenced with extensive explorations around Atlanta, GA, employing data from Google Places and correlating it with real estate listings data in the same area. However, this initial approach led to overfitting issues. Consequently, despite the sizable sample, our models yielded low accuracy scores.
Upon realizing that our initial approach, which focused solely on linear relationships, did not yield satisfactory results, we needed to reassess our understanding of the interplay between POIs and home values. To support this shift in perspective, we conducted further analysis and observed that the relationship between POIs and home values is more complex than a simple linear correlation. This insight was reinforced by examining various statistical measures, such as scatter plots and correlation matrices, which revealed that the influence of POIs on real estate returns is not strictly linear. While proximity to POIs can be a contributing factor, other variables such as neighborhood demographics, local market trends, and property characteristics also play significant roles. By acknowledging the multifaceted nature of the relationship between POIs and home values, we were able to adjust our approach and explore alternative methods to capture the true impact of POIs on real estate portfolios.
So instead, we opted for a shift in perspective.
Our exploration led us to an intriguing alternative approach. Instead of correlating POIs directly to home value, we aimed to correlate the target demographics of businesses to zip-code and tenant demographics. The premise was simple: Businesses, large or small, conduct meticulous market research before selecting a location. This choice can offer valuable demographic insights to real estate investors.
To give some broader context, real estate investment invariably requires a comprehensive understanding of numerous factors influencing the success of an investment. These include market dynamics, supply and demand, economic conditions, and demographics such as population growth, age distribution, and income levels. Traditional mortgage data, despite its value, is often limited by the frequency of updates. In rapidly changing markets, this sluggishness in data collection and delayed updates can impede investors' ability to make timely, accurate investment decisions.
To mitigate these challenges, we propose a novel methodology, leveraging points of interest (POIs) and their correlation with zip codes as proxies for demographic insights to match investors' portfolio preferences. POIs, which can range from businesses and amenities to landmarks and entertainment venues, reflect the characteristics and preferences of the local population dictated by market demands. Analyzing the distribution and types of POIs in a given area can give us a thorough understanding of the target audience and their preferences.
The rationale behind using POIs as proxies for demographic insights is that businesses invest considerable time, money, and research into understanding their target market before deciding where to open new locations. In the case of larger businesses, they possess proprietary data like customer profiles, buying patterns, and market trends, which help them identify areas with the highest potential for success.
Businesses select locations in proximity to their target demographics. If these target demographics line up with an investor's portfolio demographics, then that area could be a viable investment. Conversely, if a business closes, it may indicate shifts in demographics or market conditions. By piggybacking on businesses' extensive research and expertise, we can tap into this valuable knowledge, creating a symbiotic relationship with an investor's profile.
As an illustrative example, let's consider a real estate investor heavily invested in an area 20 minutes outside of Atlanta, GA. They own an apartment complex and various rental properties scattered throughout a specific zip code, which have particular demographics. This investor aims to expand their portfolio into other areas with similar returns.
In parallel, Starbucks, which conducts thorough market research to identify areas with high success probabilities, shares a similar interest in these demographics. It follows that if the presence of certain POIs like Starbucks, Michaels, and Jimmy Johns aligns with the investor's profile, we can infer that the demographics and market conditions are conducive to successful real estate investments for the investor.
The data utilized for this project comprises a combination of Census data, HMDA data, and Google Places data. These datasets provide valuable insights into the demographics, housing market, and amenities of different areas within the Atlanta Metro region. While we focused on over 200 zip codes in Atlanta as a proof of concept, the methodology can be applied universally to other regions.
The Census data, summarized by zip code, offered a wealth of demographic details such as population density, income levels, education levels, and housing statistics. This information helps paint a comprehensive picture of the characteristics and composition of each neighborhood or area under analysis. Understanding the demographics of an area is crucial for real estate investors as it can provide insights into the target market, rental demand, and potential property value appreciation.
The HMDA, or Home Mortgage Disclosure Act, data is another essential dataset for our project. This data, also summarized by zip codes, provides information about loan applications, approvals, rates, and types. The HMDA dataset offers a wealth of information on mortgage activity within different areas, enabling us to assess the lending environment and creditworthiness of borrowers. The HMDA data is particularly useful for many real estate use cases as it allows us to analyze loan approval rates, which can serve as an indicator of economic stability and creditworthiness of borrowers in a specific area.
To illustrate the significance of HMDA data, let's examine the map below, which displays the HMDA approval rates in the Atlanta Metro region. Lighter shades represent higher approval rates. Upon analysis, a discernable pattern emerges, especially in areas north of Atlanta, such as Buckhead, which exhibits notably higher approval rates compared to the rest of the region.
Now, let's consider another map below, which showcases the percentage of people earning more than $200,000 per year in each zip code. Remarkably, we observe a striking overlap with the HMDA approval rates map. Once again, the zip codes north of Atlanta exhibit lighter shades, indicating both higher approval rates and a higher percentage of the wealthy population.
This correlation suggests that these specific areas not only have a relatively affluent population but also signify a robust economic environment. The combination of high income levels and high approval rates indicates a healthy real estate market and potentially lucrative investment opportunities.
So, how does this information serve an investor? High approval rates are often indicative of stable economic conditions and lower credit risk, which could suggest higher property values. Furthermore, the knowledge that high approval rates align with areas where a larger percentage of the population earns over $200,000 adds an additional layer of confidence for an investor. It indicates that the area attracts a wealthier demographic that is likely to maintain steady rent payments and have a lower risk of defaulting on their mortgages. Thus, incorporating HMDA data into our strategy further refines our approach and allows for a more nuanced understanding of investment potentials.
Given the importance of the HMDA data for our project, it is essential to delve into its significance. The HMDA dataset provides a comprehensive view of mortgage activity and lending patterns within different areas, making it a valuable resource for various real estate use cases. By analyzing HMDA data, investors and real estate professionals can gain insights into the lending environment, creditworthiness of borrowers, and the overall economic stability of specific regions or neighborhoods. This information is invaluable for identifying areas with potential for investment, assessing market conditions, and making informed decisions based on credit risk and loan approval rates. Therefore, leveraging the HMDA data can significantly enhance the accuracy and effectiveness of real estate investment strategies.
Continuing our exploration, we turned our attention to the Gross Rental Yield. This crucial metric provides an idea of how much an investor could make on an investment property before considering expenses like property management, taxes, and insurance. It enables investors to evaluate the potential return on investment based solely on rental income.
To calculate gross rental yield, we divided the property's annual rental income by its purchase price or market value, then expressed it as a percentage. For instance, a gross rental yield of 7% means the rental income is approximately 7% of the property's value. Since we didn't have the sale and rental price of individual properties, we used the mean prices for each zip code.
After establishing the gross rental yield, we explored various POI categories with at least 100 locations across the Atlanta Metro Region. The bottom ten categories, displayed in red on the right of the below bar chart, are most prevalent in zip codes with a low gross rental yield. These categories include industries such as real estate, which thrive in areas with high home values and high rental prices.
Conversely, the top ten categories found in zip codes with a high gross rental yield are displayed in green on the left of the chart. The 'Trucking Company' category emerged as a standout, with an impressive 7.5% average gross rental yield across locations. Interestingly, other POI categories such as 'Pawn Shops,' 'Warehouses,' and 'Laundromats' were also among the top ten.
This data implies that these areas, despite potentially being seen as temporary living spaces, may offer lucrative investment opportunities. The presence of these business types may indicate an underserved or transient demographic. While they may not be long-term residents, they still represent a sector with housing needs. In other words, high rental yields are not necessarily linked to high-end POIs like luxury retail or gourmet restaurants. Instead, practical and essential services seem to dominate the list.
Understanding the distribution and types of POIs in these high yield areas can serve as a strong indicator of the kind of tenant an investor can expect. Consequently, it will enable investors to tailor their properties to cater to these specific demographics, leading to higher occupancy rates, stable rental income, and, ultimately, higher returns on their investment.
It is worth mentioning that while gross rental yield is a valuable metric for assessing investment potential, it is essential to consider other factors such as property expenses, market trends, and local regulations to make well-informed investment decisions. In our analysis, we focused on gross rental yield as a starting point, and by not accounting for expenses, we aimed to highlight the untapped opportunities in certain POI categories. However, in practice, investors should thoroughly evaluate all relevant aspects before finalizing their investment strategies.
Continuing our analysis with Atlanta, GA as a proof of concept, we delve into how various businesses in the area correlate with an assortment of census and mortgage data. This investigation helps us identify the demographics of the customers these businesses serve in the zip codes they occupy and align these with investor preferences.
For instance, let's consider Dollar General. The below heatmap, which displays the POI name on the X-axis and census data on the Y-axis, reveals that Dollar General is most strongly correlated with areas that have low rental/property value, a large percentage of car commuters (particularly those traveling 60 min or more to work), and a household income of $35-50k. Conversely, Dollar General stores do not typically show up in areas with incomes higher than $200k or high median property values.
On the other hand, a store like 'Hollywood Feed,' a pet store chain, correlates highly with households that make over $200k. These correlations allow investors to match their interests with specific demographics served by these businesses.
To streamline this concept, we have created a function using K Nearest Neighbor (KNN). This function accepts an investor's current zip code and desired demographic profile, and it outputs a recommendation of 'K' number of zip codes that share a similar demographic profile. Additionally, it suggests prevalent POIs within that zip code.
The process begins with the input of a zip code and features an investor is interested in. The data is then used in a K-Nearest Neighbors (KNN) algorithm to identify the k most similar zip codes based on the provided columns' profiles. Moreover, the function is scalable and can handle large datasets efficiently, making it suitable for large data sets and real-world scenarios.
To make these concepts more digestible, we have visualized the results of the KNN algorithm using Principal Component Analysis (PCA) in a 2D space. PCA helps us capture the most important patterns and variances in the data. This visual aid confirms that we are identifying the “closest” relationships between zip codes. The gray dots represent all the zip codes in our dataset, the red dot is the selected zip code, and the blue dots represent the ”closest” zip codes.
We used Plotly Dash to develop a tool that leverages businesses' proprietary information and demographic analysis expertise for the benefit of real estate investors. It is an inexpensive solution to identify areas that align with investor profiles and demonstrate significant investment potential. It also uncovers areas of future growth potential by identifying areas that match the preferences of successful businesses.
Our approach is cost-efficient, as it capitalizes on businesses' extensive data, reducing the need for expensive data acquisition. Furthermore, by using POIs as proxies for demographic information, we overcome the limitations of slow and infrequent data updates, enabling investors to stay ahead of market trends.
Our project underscores the power of data science in revolutionizing real estate investment. By aligning data from various sources and correlating them in novel ways, we have unearthed valuable insights that promise to redefine real estate investment strategies.
We have not only drawn connections between seemingly unrelated variables but also created a powerful, user-friendly tool that offers real-time, granular insights to real estate investors. This interactive dashboard allows investors to explore various aspects of zip codes, from housing and commuting attributes to demographic and earnings attributes.
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Show what you’ve learned from the Professional Certificate Program in Data Science.
What you'll learn.
How to apply the knowledge base and skills learned throughout the series to a real-world problem
Independently work on a data analysis project
To become an expert data scientist you need practice and experience. By completing this capstone project you will get an opportunity to apply the knowledge and skills in R data analysis that you have gained throughout the series. This final project will test your skills in data visualization, probability, inference and modeling, data wrangling, data organization, regression, and machine learning.
Unlike the rest of our Professional Certificate Program in Data Science, in this course, you will receive much less guidance from the instructors. When you complete the project you will have a data product to show off to potential employers or educational programs, a strong indicator of your expertise in the field of data science.
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Learn probability theory — essential for a data scientist — using a case study on the financial crisis of 2007–2008.
Learn inference and modeling: two of the most widely used statistical tools in data analysis.
A focus on several techniques that are widely used in the analysis of high-dimensional data.
The School of Information is UC Berkeley’s newest professional school. Located in the center of campus, the I School is a graduate research and education community committed to expanding access to information and to improving its usability, reliability, and credibility while preserving security and privacy.
The School of Information offers four degrees:
The Master of Information Management and Systems (MIMS) program educates information professionals to provide leadership for an information-driven world.
The Master of Information and Data Science (MIDS) is an online degree preparing data science professionals to solve real-world problems. The 5th Year MIDS program is a streamlined path to a MIDS degree for Cal undergraduates.
The Master of Information and Cybersecurity (MICS) is an online degree preparing cybersecurity leaders for complex cybersecurity challenges.
Our Ph.D. in Information Science is a research program for next-generation scholars of the information age.
The School of Information's courses bridge the disciplines of information and computer science, design, social sciences, management, law, and policy. We welcome interest in our graduate-level Information classes from current UC Berkeley graduate and undergraduate students and community members. More information about signing up for classes.
Research by faculty members and doctoral students keeps the I School on the vanguard of contemporary information needs and solutions.
The I School is also home to several active centers and labs, including the Center for Long-Term Cybersecurity (CLTC) , the Center for Technology, Society & Policy , and the BioSENSE Lab .
I School graduate students and alumni have expertise in data science, user experience design & research, product management, engineering, information policy, cybersecurity, and more — learn more about hiring I School students and alumni .
Capstone projects are the culmination of the MIDS students’ work in the School of Information’s Master of Information and Data Science program.
Over the course of their final semester, teams of students propose and select project ideas, conduct and communicate their work, receive and provide feedback, and deliver compelling presentations along with a web-based final deliverable.
Join us for an online presentation of these capstone projects. Six teams will present for twenty minutes each, including Q&A.
A panel of judges will select an outstanding project for the Hal R. Varian MIDS Capstone Award .
Join the online showcase
Dr. Julia Meo holds a Ph.D. in physics from the University of Pennsylvania. Her professional experience ranges from creating recommender systems using natural language processing to time series analysis, and forecasting. During her time at Zillow, Dr. Meo’s research has focused on creating data products that characterize the housing economy. Her in-depth analysis of housing market dynamics, pricing trends, and investment opportunities has provided valuable insights to inform policymakers, real estate professionals, and investors. Her work has been instrumental in shaping strategies and decision-making in the real estate industry, driving growth and innovation.
Ryan Neo, MIDS ’20, is a data science leader with over a decade of experience in finance, consulting and tech. He currently leads a data science team at Meta focusing on advertiser products. He enjoys consulting on academic and nonprofit research projects in his spare time. Ryan notes that his favorite course within the MIDS program is DATASCI 241. He currently lives in San Francisco with his wife, daughter, and cat.
Pauline Wang is a data science manager with extensive expertise in machine learning and data analytics. In her current role as manager of machine learning triage at IBM Watson Orders, she leads the triage data analytics capability to guide and prioritize the development of AI-powered voice agents for quick service restaurants (QSRs). She has been instrumental in building tools and processes that have expanded the team’s capability from just 5 to over 100 live restaurants. Prior to joining IBM, Pauline served as director of research at Elliott Management Corporation, where she leveraged unstructured data to identify investment opportunities. When she is not busy training robots, Pauline enjoys dancing, cooking, and keeping up with the latest trends in AI technologies.
Spring 2023 MIDS Project Descriptions
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Properties features in saint petersburg, russia, frequently asked questions about real estate in st. petersburg, in what areas of st. petersburg real estate is purchased most often, what documents do i need to have to buy property in st. petersburg, what are the restrictions and special conditions for foreigners buying apartments and houses in st. petersburg.
The St. Petersburg housing market is somewhat competitive. Homes in St. Petersburg receive 2 offers on average and sell in around 41 days. The median sale price of a home in St. Petersburg was $435K last month, down 4.4% since last year. The median sale price per square foot in St. Petersburg is $345, down 0.43% since last year.
In August 2024, St. Petersburg home prices were down 4.4% compared to last year, selling for a median price of $435K. On average, homes in St. Petersburg sell after 41 days on the market compared to 21 days last year. There were 397 homes sold in August this year, down from 465 last year.
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5521 80th ST N #310 St Petersburg, FL 33709
Listed By CENTURY 21 Jim White & Associates
300 Beach Drive NE Unit 1101 Saint Petersburg, FL 33701
Courtesy Of Coldwell Banker St Petersburg NE
1401 5th Street N Saint Petersburg, FL 33704
Courtesy Of Coldwell Banker Winter Park
423 55th Avenue St Pete Beach, FL 33706
Courtesy Of The Toni Everett Company
12400 Capri Circle N Unit A Treasure Island, FL 33706
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7850 2 Avenue S St Petersburg, FL 33707
Courtesy Of Keller Williams St. Pete Realty
3500 35th Street N Saint Petersburg, FL 33713
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6083 Bahia Del Mar Circle 361 Saint Petersburg, FL 33715
7510 Sunshine Skyway Lane S P4 Saint Petersburg, FL 33711
Courtesy Of ADDvantage Real Estate
2960 59th Street S 102 Gulfport, FL 33707
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400 150th Ave # 304 Madeira Beach, FL 33708
Listed By CENTURY 21 Beggins Enterprises
130 34th Ave. N. St. Petersburg, FL 33704
Listed By CENTURY 21 Real Estate Champions
6129 Leeland Street S Saint Petersburg, FL 33715
237 7th Ave N St Petersburg, FL 33701
1330 Cherry Street NE Saint Petersburg, FL 33701
400 150th Ave # 302 Madeira Beach, FL 33708
400 150th Ave # 305 Madeira Beach, FL 33708
6495 Shoreline Dr. #8202 St. Petersburg, FL 33708
3146 29th Avenue N 203 Saint Petersburg, FL 33713
Courtesy Of Dalton Wade, Inc.
18053 2nd St. E. Redington Shores, FL 33708
200 45th Avenue Ne Saint Petersburg, FL 33703
Courtesy Of Coastal Properties Group International, LLC
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Data-science-capstone-real-estate, problem statement.
A banking institution requires actionable insights into mortgage-backed securities, geographic business investment, and real estate analysis. The mortgage bank would like to identify potential monthly mortgage expenses for each region based on monthly family income and rental of the real estate. A statistical model needs to be created to predict the potential demand in dollars amount of loan for each of the region in the USA. Also, there is a need to create a dashboard which would refresh periodically post data retrieval from the agencies. The dashboard must demonstrate relationships and trends for the key metrics as follows: number of loans, average rental income, monthly mortgage and owner’s cost, family income vs mortgage cost comparison across different regions. The metrics described here do not limit the dashboard to these few.
Second mortgage: Households with a second mortgage statistics
Home equity: Households with a home equity loan statistics
Debt: Households with any type of debt statistics
Mortgage Costs: Statistics regarding mortgage payments, home equity loans, utilities, and property taxes
Home Owner Costs: Sum of utilities, and property taxes statistics
Gross Rent: Contract rent plus the estimated average monthly cost of utility features
High school Graduation: High school graduation statistics
Population Demographics: Population demographics statistics
Age Demographics: Age demographic statistics
Household Income: Total income of people residing in the household
Family Income: Total income of people related to the householder
Data import and preparation:.
1.Import data.
2.Figure out the primary key and look for the requirement of indexing.
3.Gauge the fill rate of the variables and devise plans for missing value treatment. Please explain explicitly the reason for the treatment chosen for each variable.
4.Perform debt analysis. You may take the following steps:
a) Explore the top 2,500 locations where the percentage of households with a second mortgage is the highest and percent ownership is above 10 percent. Visualize using geo-map. You may keep the upper limit for the percent of households with a second mortgage to 50 percent
b) Use the following bad debt equation: Bad Debt = P (Second Mortgage ∩ Home Equity Loan) Bad Debt = second_mortgage + home_equity - home_equity_second_mortgage c) Create pie charts to show overall debt and bad debt
d) Create Box and whisker plot and analyze the distribution for 2nd mortgage, home equity, good debt, and bad debt for different cities
e) Create a collated income distribution chart for family income, house hold income, and remaining income
a) Use pop and ALand variables to create a new field called population density
b) Use male_age_median, female_age_median, male_pop, and female_pop to create a new field called median age c) Visualize the findings using appropriate chart type
a) Analyze the married, separated, and divorced population for these population brackets
b) Visualize using appropriate chart type
Please detail your observations for rent as a percentage of income at an overall level, and for different states.
Perform correlation analysis for all the relevant variables by creating a heatmap. Describe your findings.
Data pre-processing:.
• Highschool graduation rates
• Median population age
• Second mortgage statistics
• Percent own
• Bad debt expense
Data modeling :.
a) Run a model at a Nation level. If the accuracy levels and R square are not satisfactory proceed to below step.
b) Run another model at State level. There are 52 states in USA.
c) Keep below considerations while building a linear regression model. Data Modeling :
• Variables should have significant impact on predicting Monthly mortgage and owner costs
• Utilize all predictor variable to start with initial hypothesis
• R square of 60 percent and above should be achieved
• Ensure Multi-collinearity does not exist in dependent variables
• Test if predicted variable is normally distributed
a) Box plot of distribution of average rent by type of place (village, urban, town, etc.).
b) Pie charts to show overall debt and bad debt.
c) Explore the top 2,500 locations where the percentage of households with a second mortgage is the highest and percent ownership is above 10 percent. Visualize using geo-map.
d) Heat map for correlation matrix.
e) Pie chart to show the population distribution across different types of places (village, urban, town etc.)
COMMENTS
Real-Estate. Capstone Project in Simplilearn. Business understanding and Data understanding are very critical first couple of steps for any data science project. Read the information given below and also refer to the data dictionary provided separately in an excel file to build your understanding. Problem Statement:
Developing a house price prediction model is a great way to start. There's a ton of accessible housing data online, e.g., sites like Zillow and Airbnb, and these datasets are perfect for executing this type of project. Zillow's free datasets are a popular choice; the Zillow Home Value Index (ZHVI) is a smoothed, seasonally adjusted average ...
DESCRIPTION. Problem Statement. A banking institution requires actionable insights into mortgage-backed securities, geographic business investment, and real estate analysis. The mortgage bank would like to identify potential monthly mortgage expenses for each region based on monthly family income and rental of the real estate.
Data Science Methods for Real Estate, including index construction, automated valuation, cluster analysis, and time series forecasting (ARIMA, VAR, and VECM). ... online video conferences. Office hours are one-on-one online video conferences. The capstone project is presented online - students are invited to (but not required to) attend the ...
Data Science Blog > Capstone > Enhancing Analysis with Model Interpretability: ... In practical applications, such as real estate, models are used to identify candidate single-family residences (SFR) for purchase and rent. Cap rate, the rate of return on a property based on the income the property is expected to generate, crucially aids this ...
Best Data Science Capstone Project Ideas - According to Skill Level. Data science capstone projects are a great way to showcase your skills and apply what you've learned in a real-world context. Here are some project ideas categorized by skill level: Beginner-Level Data Science Capstone Project Ideas. 1. Exploratory Data Analysis (EDA) on a ...
Data Science capstone projects batch #23. by Ekaterina Butyugina. ... Novalytica, a data science startup with real estate expertise, is looking into helping investors address these challenges with custom-tailored data and machine learning solutions. To do this, they gave our team access to several datasets concerning energy consumption and ...
Real Estate Capstone Project. Contribute to shiva8826/Data-Science-Capstone-Project development by creating an account on GitHub.
One of the areas of investment that benefits from a data-driven approach is real estate. To explore the impact data science can have on this form of investment, we partnered with Haystacks, a real estate investment strategy company. This blog post dives into our innovative approach, specifically focusing on the interplay between investor ...
Project Overview. This semester we worked with REX, a real estate technology company that is trying to bring innovation to an industry that hasn't seen much of it over the past 50+ years. In the spirit of REX's mission, our goal was to address these two weaknesses of traditional real estate indices.
Explore and run machine learning code with Kaggle Notebooks | Using data from Real Estate_simpilearn Project. Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. Learn more. OK, Got it. Something went wrong and this page crashed!
To become an expert data scientist you need practice and experience. By completing this capstone project you will get an opportunity to apply the knowledge and skills in R data analysis that you have gained throughout the series. This final project will test your skills in data visualization, probability, inference and modeling, data wrangling ...
Online. Capstone projects are the culmination of the MIDS students' work in the School of Information's Master of Information and Data Science program. Over the course of their final semester, teams of students propose and select project ideas, conduct and communicate their work, receive and provide feedback, and deliver compelling ...
Real Estate Capstone Project. Contribute to shiva8826/Data-Science-Capstone-Project development by creating an account on GitHub.
5+ Bedrooms. 112. 119. + 6.3%. Summary: The Saint Petersburg housing inventory by bedroom type for August 2024 compared to the previous month: The inventory of 1 bedroom homes increased by 5.3%, 2 bedroom homes increased by 5.7%, 3 bedroom homes increased by 3.1%, 4 bedroom homes increased by 2.1%, and 5+ bedroom homes increased by 6.3%.
Real Estate Prices & Venues Data Analysis of London - mtk12/IBM-Data-science-capstone-project
Find Residential properties for Sale in Saint Petersburg, Russia Large selection of residential properties in latest listings Actual prices Photos Description and Location on the map.
The St. Petersburg housing market is somewhat competitive. Homes in St. Petersburg receive 2 offers on average and sell in around 42 days. The median sale price of a home in St. Petersburg was $415K last month, up 0.4% since last year. The median sale price per square foot in St. Petersburg is $334, up 0.1% since last year.
View our Saint Petersburg real estate area information to learn about the weather, local school districts, demographic data, and general information about Saint Petersburg, FL. ... Century 21 Real Estate LLC fully supports the principles of the Fair Housing Act and the Equal Opportunity Act. Each office is independently owned and operated.
Data Import and Preparation: 1.Import data. 2.Figure out the primary key and look for the requirement of indexing. 3.Gauge the fill rate of the variables and devise plans for missing value treatment. Please explain explicitly the reason for the treatment chosen for each variable.