Our course teaches the necessary skills to utilize large datasets to determine fair transaction prices, forecast future returns and how to analyze locations with geographic information systems (GIS).

The Bootcamp module teaches Python, Pandas, and Scikit-Learn.

The Real Estate Data Science module covers web harvesting, index construction, automated valuation models, time series forecasting, and cluster analysis for real estate. We’ll also discuss the business opportunities, trends, and hot companies to watch in real estate data science.

The Geographic Information Systems module covers spatial and location analysis techniques (see “What’s the “Geographic Information Systems” module about?” question).

We’d like to walk through the course content, and show you demonstrations of the analysis techniques you’ll learn on a video call. Please contact us so we can set this up.

Here’s an outline of the bootcamp sessions

[B1] Python Bootcamp

• Introduction (including cmd / terminal, sublime text)
• Running your first program
• Math operations, data types
• Variables
• Strings & Text
• User Input, casting
• Reading & Writing Files
• Functions
• And, Or, Not
• If, Elif, Else
• Lists (including list comprehensions)
• Loops
• Dictionaries (including sorting, dictionary comprehensions)
• Lambda functions
• Python Debugger (PDB)

[B2] Pandas Bootcamp

• creating Series & DataFrames
• reading & writing datasets (csv, Excel, pickle, etc.)
• making new columns, changing data, deleting,
• astype
• str methods
• .apply()
• selecting, slicing, filtering
• summary statistics, quantiles, etc.
• sorting
• dealing with duplicates & blanks
• groupby()
• merge, join, concat
• visualizing / plotting / charting

[B3] Scikit-Learn Bootcamp

• Introduction (concepts, data, algorithms)
• Classification & Regression
• Statsmodels
• OLS, Ridge
• Categorical & Interaction terms
• SVM, Logistic Regression, Random Forests, Nearest Neighbors
• XGBoost
• Feature Engineering
• Evaluating Model Fit
• Avoiding Overfitting (including cross-validation)
• Dimensionality Reduction (PCA)

All 11 weeks of the course are conducted in Python. So, at the end of the first week, you have a basic understanding on which to continue to learn - and you'll continue to learn more Python in each session of the course. Similarly, we continue to build on your knowledge of Pandas from the second week through to the end of the course, and of data science methods from the third week through to the end of the course.

You have multiple opportunities to learn. First, hands on during the live class sessions - ask any questions, share your screen if you get stuck, work on exercises live and share your answers. Second, you'll learn a lot as you work on the assignment (there's an assignment every week). When you submit the assignment, you will receive suggested solutions. And, you can attend the weekly live small group TA sessions to go over the week's assignment and material. Finally, you'll really get a grasp of the material as you build your own hands on capstone project, using a dataset of your choosing and answering a question of relevance to you - but using the techniques from the course.

In this module, we teach methods for spatial and location analysis.

In the first session, we'll teach Quantum GIS (QGIS). This is a point-and-click graphical user interface program. We'll work through background concepts and knowledge for GIS and spatial analysis, including coordinate systems, file types (“shapefiles”, KML files, boundary files, etc.), layers, and more. We'll work through an example where we load a base street map, property transaction data (with locations), and train station locations and types. We'll then answer a question - can we show that property prices per square foot are higher near train stations? We will be able to show that yes, they are, except for light rail stations. You'll be able to build on this knowledge and use QGIS to load and analyze other information layers e.g., demographics, crime rates, etc. in combination with real estate data.

In the second session, we teach GeoPandas and other Python packages for spatial analysis. Here, we work through 3 types of questions. (1) What's the distance from an asset to a fixed point (e.g., "how far is a given property from the centre of the central business district") (2) What's the distance from an asset to the minimum of a set of points (e.g., "how far away is the property from its nearest train station") (3) Location scoring (assigning a numerical score for a given attribute e.g., school quality to each (latitude, longitude) coordinate).

These methods are best illustrated on a video call. Please contact us so we can share some demonstrations of the skills you’ll learn.

Yes! We emphasize examples from residential real estate due to greater public data availability. But, the techniques are applicable across sectors. Where data is limited for commercial real estate, we can suggest slight adjustments to application of the techniques from this class.

If you have a specific project you’d like to work on, we’d be excited to help make this your capstone presentation. Do contact us to discuss further.

Yes - the algorithms we cover can be applied for different geographies, but need to be localized. We’ll discuss this in class e.g., what factors you might want to include for particular geographies. To be clear, the algorithm is the same, but the input factors for the algorithm need to be localized by geography.

Yes - you’ll learn Python, Pandas, and Scikit-Learn in the Bootcamp module. And, you’ll learn domain specific applications of data science to real estate, as well as spatial analysis methods. The course is totally hands on - in each class, you’ll be writing and running code alongside us to execute these analyses. You’ll receive the code files, and will be writing programs and running analyses by your own hand for the assignments and capstone project - delving in-depth into these methods with real data sets.

This is a domain-specific course - we teach specific techniques and methods for using data science in real estate (rather than general data science methods). Many data scientists and analysts in real estate companies have participated in our program to learn these techniques!

We also point out the following salient features of our course:

• We conduct the course in Python. Python is more broadly used than R and has a greater range of applications beyond just data science (i.e., web harvesting, robotic process automation, and more).
• Our course includes a bootcamp module in Python, Pandas, and Scikit-Learn. We’ll help you get set up, and make sure you learn all the required knowledge to succeed in the real estate data science portion of the course.
• Our default mode of attendance is live interactive classes.
• We have a lot of support! We’ll help you set up the software on your machine. You’ll get to work on assignments and see solutions, join our live small group TA sessions, ask questions in our slack channel or over email, and even book optional TA one-on-ones if you’d like.
• Ours is an in-depth, Masters’-level course that runs 11 weeks. At the end of the course, you’ll be able to apply these techniques to a real world data set.
• You’ll get to work on a capstone project and present it live. This will be based on a dataset of your choosing, with guidance from the course instructors. This could be shown to potential employers, could be an opportunity to build an analysis directly relevant to your firm, or can be the start of a product that you can build a Proptech company around!

You’ll be able to assemble datasets for analysis from a variety of sources. You’ll learn to harness large data sets to determine fair transaction prices, forecast future returns and how to analyze locations with geographic information systems (GIS). So, you could build a fully data-driven investment strategy in real estate. Your investment recommendations would harness an amount of data exceeding the limits of human cognition - you could find market-beating investments, produce industry-leading insights, and gain in-demand skills that position you ahead of the curve.

To learn more, read “5 Ways to Apply Data Science to Real Estate” on Towards Data Science, authored by PropertyQuants’ CEO Nelson Lau, PhD, CFA.

For real estate agents / brokers / realtors, here are some potential use cases:

• Automatically keeping track of huge numbers of property listings, auctions, transactions, and other data; smartly filtering and selecting the most relevant opportunities suiting a particular clients’ goals. This uses web harvesting, automated valuation, python, and pandas.
• Identifying possible off-market inventory. By using automated valuation models in combination with transaction data, you can estimate equity and determine which owners might be most willing to sell their properties.
• Making data-driven recommendations about buying, selling, or leasing prices based on big data sets.
• Determining the current market cycle i.e., market timing indicators based on cluster analysis of price indices and macroeconomic data.
• Grouping a city into submarkets, to help clients find properties similar to a desired initial listing.
• Visualizing and communicating locality information or location quality across multiple dimensions on a map (e.g., showing school quality, commute times, population changes, etc. on a geographic information system).
• Forecasting submarket performance, to identify the areas and asset classes that will outperform or underperform - so you can help build a reputation for helping your clients beat the market.

Overall, this course will enable you to operate at scale (working with very large numbers of listings / transactions to find the best deals or make the most informed recommendations) and to stand out from the crowd as a tech-enabled, data-driven operator - it will give clients a reason to choose to work with you rather than just any run of the mill agent, and a reason to listen to and follow your advice rather than their own instincts and judgment.

Here are some applications of the course content for Real Estate Investment Funds:

• Forecasting property price growth, income growth, and macroeconomic time series to pick the best markets to invest in, guide deal sourcing, and outperform peers.
• Building valuation models based on big data approaches to determine fair prices to buy, sell, or lease individual assets, to maximize income and returns.
• Assess portfolio diversification by identifying whether assets are concentrated in particular submarkets (e.g., as constructed via cluster analysis).
• Time acquisitions and divestments based on market cycle analysis, to maximize IRRs.
• Identify and statistically validate drivers of real estate performance, to inform and justify investment decision making and improve returns.
• Automatically track and assess potential returns across a large number of potential markets, expanding investment opportunities and improving return possibilities.
• Communicate location information to investors and committees using Geographic Information Systems, to explain why chosen properties will outperform.

There is a great deal of interest in applying Data Science to Real Estate investment, and successfully establishing a strategy in this area is a game changer that will help your fund distinguish itself from the competition and attract investment dollars.

Automated valuation methods for real estate are gaining popularity around the world, and are seeing increasing take-up from investors, lenders, and owners. With the knowledge from this course, appraisers and valuers can:

• Expand the scale of their business by expanding into automated techniques that can quickly compute large numbers of valuations.
• Deliver superior, differentiated, more accurate analyses to clients by combining traditional valuation techniques with AI-powered automated methods.
• Produce more informed analyses that exceed human limitations by writing programs to collect huge amounts of data, and using all this information to produce accurate automated valuations.
• Justify the comparables selected for valuation using data science approaches to determine similarity.
• Rigorously incorporate spatial data / locality factors into valuations.
• Forecast time series using machine learning methods (e.g., rental growth) to produce more accurate income approach valuations.

Data-driven methods are being demanded by clients, and incorporating these methods into appraisal practice will future-proof your business.

Here are some ways property developers could use the methods in the course:

• Selecting the best development sites by computing, studying, and visualizing location specific factors (e.g., population characteristics, commute times, zoning).
• Communicating location quality to investment committees or out of town buyers.
• Determining fair market pricing for properties developed, including forecasting price and rental growth over time.
• Understanding property submarkets, to determine typical characteristics and supply or scarcity of property types at various locations.
• Building specific property price indices to understand and communicate price trends and identify divergences and opportunities.
• Determining market cycles based on macroeconomic condition scores, to optimally time land acquisitions and development launches and improve profitability.
• Collating large data sets to find specific redevelopment opportunities matching specified criteria (e.g., under-utilization of floor area ratios, current pricing relative to neighborhood).

There is a lot of scope for a data-science-enabled property developer to improve profitability by making smarter investment and disposition decisions, and by better communicating the value proposition at their development locations.

Proptech is a really broad space, covering any kind of digital innovation related to real estate. Here are some examples of how various proptech companies could use the techniques in the course:

• Data providers can move up the value chain by selling analytics such as property price indices, forecasts, automated valuations, or location scoring.
• Listing websites could add features to drive traffic e.g., home valuations, neighborhood analysis, or price indexation.
• Fractional investment sites, instant buyers, or alternative property finance companies could use automated valuation to produce accurate property valuations to power their business.
• Tech-enabled brokerages can help their agents filter and select the most relevant offerings for a client, use data driven methods to recommend appropriate buying, selling, and leasing prices, or even identify potential off-market inventory (see “How does this course help property agents / brokers / realtors”).
• New business models can be launched with data science applied to real estate e.g., quantitative investment funds.

Looking just at banks’ business lines related to real estate lending and mortgages, some potential applications are:

• Speeding up and increasing the scale of loan origination by instantly providing indicative property valuations.
• Enabling automated valuation of entire portfolios of loans or mortgage-backed securities, to stress-test exposures or identify trading opportunities.
• Accelerating loan underwriting by automatically gathering relevant datasets, then studying, scoring, and visualizing location factors and using data driven approaches in analysis or valuation of collateral.

Data science methods for real estate financing allow efficient operation at scale, allowing banks to expand their businesses and find more profit opportunities.

Individual real estate investors can use the techniques in the course to beat the market!

• Automatically scour all available property listings, then find the most undervalued investment opportunities.
• Estimate the potential profit from property alterations or improvements, and only execute those with the highest return on investment.
• Compare a huge number of locations and identify the best markets to invest, including forecasting price and rental growth over time.
• Time the market by assessing the current market cycle based on macroeconomic conditions.
• Wanted to buy a property, but lost the deal to someone else? Identify similar alternative opportunities within a city.
• Determine fair prices to bid on or market properties.
• Assess portfolio diversification or concentration within similar clusters.

Absolutely! In the KPMG Global PropTech Survey 2018, 49% of participants thought that artificial intelligence, big data, and data analysis were the technologies likely to have the biggest impact on the real estate industry in the long term. Join our course to learn more about these disruptive business opportunities, and learn hands-on how to construct these analyses. Even if your plan is to hire technical staff or consultants, or if you only need to understand the companies that work in this space, you’ll benefit greatly from gaining an in-depth understanding of these methods, knowing what questions to ask and what work to ask for, and knowing what’s possible with these techniques and technologies.

We’ll discuss this in session 1 of the Real Estate Data Science module, where we conduct a seminar-style class about disruptive business models, trends, and hot companies to watch.

There is information about this in “5 Ways to Apply Data Science to Real Estate”. You can also learn more about this in a recent MIT Real Estate Club Webinar we participated in (the focus is more about Proptech broadly, but does cover some opportunities for data science in real estate as well.)

Yes, you can customize the course to suit your exact needs, and we’ll adjust the pricing accordingly. To discuss further, contact us .

We don’t recommend this, because a long gap between classes could be detrimental to the learning process. Nonetheless, this is possible. To discuss further, contact us .

We replicate the experience of attending a live university course! The default option is live sessions via Google Meet. You’ll receive slides before each session (with exercise answers removed). In class we’ll work hands-on on programming and data science exercises. Share your screen if you get stuck (we’ll provide live help), ask questions, and discuss topics with other course participants. Full slides and code files are provided after each session. You’ll also be able to review the video recording of the class as many times as you’d like until 6 weeks after the last class. And, there will be an assignment each week. Submit your answers to us and we’ll share the suggested solutions. There are also small group TA sessions you can book in which an assistant instructor will answer any questions and work through the assignment. The course culminates in a live capstone project presentation. (See “Capstone Project” FAQs.)

We also offer “video attendance” and “self directed” options.

In this option, participants will not attend the live class, but instead have full flexibility to view the recorded session from the class date until 6 weeks past the end of the course. “Video attendance” participants will still get to attend the live small group TA sessions. Participants will receive all materials, assignments, and code files, and will have the opportunity to present the capstone project live, with two 20-minute one-on-one sessions to help prepare for this.

This option is for those who want to start the course instantly, or progress at their own pace. We provide immediate access to all the historical recordings and materials at one go. There are no live small group TA sessions in this option, but you can book our TAs for one-on-one consultation for a nominal fee. You will still be able to submit the assignments and get suggested solutions, post questions to our message board, and present the capstone project live.

The default option is to participate in live classes. These are live online video conference meetings in which you’ll be able to work on exercises, share your screen, ask questions, participate in breakout rooms, and speak with other course participants.

We offer options for attending the course by “video attendance” or in a “self directed” (see What is the “video attendance” option? and What is the "self directed" option?)

"Live classes" participants will get access to the video recordings of all sessions, which can be viewed at any time up to 6 weeks after the last class. You can use these to catch up. You’ll also have access to the full slides and class materials. And, you can attend our small group TA sessions for assistance, or even book our TAs for personalized one-on-one help.

The default option is to attend our course by “live classes”.

If you do have scheduling conflicts with our live sessions, you can join our course by “video attendance” or “self directed” by historical video. These options provide great timing flexibility (see What is the “video attendance” option? and What is the "self directed" option?).

Yes! This isn’t the default option (the default is “live classes”). But, you can join us “self directed” by historical video and get all the course content instantly. (see What is the "self directed" option?)

If you take all modules of the course, this will run 11 weeks, with a capstone project presentation about 6 weeks after the last class.

In the bootcamp module (first 3 weeks), there are two 3-hour sessions each week, totalling 6 hours of class. And, we recommend attending a TA small group session (about 1 hour). We strongly recommend doing the assignment as well. This could take anywhere between 8 hours (for those new to programming) to 1 hour (for those with significant background in programming).

After the bootcamp module, there is one class each week (around 3 hours). Do continue to attend the TA sessions (1 hour), and work on the assignments (probably 4 hours a week - this varies by participant).

We run 2 classes per calendar quarter - these alternate across time zones. Please contact us to find out more about the next course run schedule.

We also offer a “self directed” option for participants who want to start immediately. (see What is the "self directed" option?)

Yes, if there is sufficient minimum group size. Please contact us to find out more.

Our course is conducted in Python. This is perhaps the world’s most popular programming language for data scientists, with a large user community and many packages for machine learning. Python also has the flexibility to be used for other tasks including web harvesting, robotic process automation, and more.

We’ll use Python and a number of add-on packages for Python (numpy, scipy, pandas, scikit-learn, statsmodels, matplotlib, xgboost, pmdarima, lxml, geopandas, and more). We also use Quantum GIS.

We’ll cover how to code from a text editor (Sublime Text) and also via Jupyter notebooks. Participants with existing familiarity are welcome to use IDEs e.g., PyCharms, Visual Studio (but this is not explicitly covered in the course).

As the course is conducted online, you will need a stable broadband internet connection. Your computer should come with a microphone and webcam to enable full interactivity in the live sessions, TA sessions, and the project presentation.

Here are guidelines on required hardware / operating systems:

• Modern Operating System:
  • Windows 7 or 10, 64-bit
  • Mac OS X 10.14 or higher, 64-bit
  • Linux: RHEL 6/7, 64-bit (almost all libraries also work in Ubuntu / Debian)
• x86 64-bit CPU (Intel / AMD64 architecture)
• 4 GB RAM (Recommended: 8GB)
• 30 GB free disk space (Recommended: SSD)

(If you are using 32-bit architecture, you will still be able to participate in the course, but will not be able to run one specific algorithm on your local machine. You will still learn the overwhelming majority of the content.)

If you have questions about recommended hardware specifications, please contact us .

We will provide detailed instructions for setup. Also, before the first class, we will schedule a live video call in which we will walk through the software setup with you and make sure that you are able to run all the required programs. This will also ensure that you will be able to program and run your own analyses independently even after the course concludes.

No! All the software used in this course is open-source and freely available.

For in-class exercises and assignments, we use real world real estate transaction datasets from publicly available sources. In session 1 of the course, we’ll discuss where you can find free publicly available data sets for your projects or further exploration. We do not sell data for the course.

Yes! More than two-thirds of our participants join our course without pre-existing knowledge of Python, Pandas, or Scikit-Learn (and without knowledge of other programming languages), and are able to learn what they need from our bootcamp module, and go on to succeed in the course. In this case, we strongly recommend attending our bootcamp, attempting all the assignments, and participating in the TA sessions.

If you have significant existing knowledge or Python, Pandas, or Scikit-Learn, you can omit those lessons from the course and we’ll adjust the pricing accordingly.

This could be useful as a refresher. Have a look at “What is covered in the Python, Pandas, Scikit-Learn bootcamp module?” to help decide if the content of each of these classes would be useful to you. You can pick and choose whether or not you’d like to attend each of the bootcamp sessions - you do not have to sign up for the whole module. Pricing will be adjusted accordingly. Contact us and we’ll help figure out the right package for you.

It’s helpful to have a high-school equivalent pre-knowledge of statistics. But, this is a hands-on course, focusing on applying these methods to real estate specific analyses. We’ll show the underlying mathematics sometimes - but only to help explain the concepts. We’ll explain the concepts needed in the course, but more importantly, we’ll work through applications and will provide the code files for the techniques we teach - so you can apply these methods to real data sets and get results.

A knowledge of statistics at a high-school level or equivalent is helpful. General computer skills are helpful. If you haven’t had the opportunity to work with Python, Pandas, or Scikit-Learn, join us for the bootcamp!

We’ve had participants from institutional real estate - private equity real estate funds, pension funds, REITs, and other investment and development firms). Appraisers and valuators have found the course immensely useful. Numerous participants have come from Proptech - listing websites, location intelligence providers, technology-enabled brokerages, and more. We’ve also had data scientists interested in learning the domain-specific applications of machine learning to real estate. And, we’ve had individual investors and entrepreneurs join the course as well!

Yes - once you registered for the course, we share a list of optional pre-work references you can work through as additional preparation for the course. This isn’t required, but can be helpful if to get a head start on some of the course content.

• Live assistance with software setup
• In the live classes, you can ask questions and get immediate answers, share your screen if you get stuck, work on exercises in real time, participate in breakout room activities, and converse with the instructors, TAs, and other course participants
• Live small group TA sessions
• Submit assignments and receive suggested solutions
• Ask questions in slack community channel
• E-mail with questions - these are answered on a best efforts basis, and you will get access to a questions & answers document
• Two 20-minute one-on-one sessions to help prepare for capstone project presentation
• (optional) Book TAs for one-on-one consultation, at a nominal fee.

• Live assistance with software setup
• Live small group TA sessions
• Submit assignments and receive suggested solutions
• Ask questions in slack community channel
• E-mail with questions - these are answered on a best efforts basis, and you will get access to a questions & answers document
• Two 20-minute one-on-one sessions to help prepare for capstone project presentation
• (optional) Book TAs for one-on-one consultation, at a nominal fee.

• Live assistance with software setup
• Submit assignments and receive suggested solutions
• Ask questions in slack community channel
• E-mail with questions - these are answered on a best efforts basis, and you will get access to a questions & answers document
• Two 20-minute one-on-one sessions to help prepare for capstone project presentation
• (optional) Book TAs for one-on-one consultation, at a nominal fee.

In these live sessions, our assistant instructors will answer any questions participants bring up about the course material. They’ll also work through the assignment solutions, and explain the code and techniques used.

Our participants come from across the world, so in every course run we will offer a spread of timings. Regardless of where you are, you will find at least one TA session that suits your waking hours (we cannot guarantee specific times within the day though).

Yes! You can book our TAs for one-on-one consultation for a nominal fee. This can be to work through the course material, assignments, or help with the capstone project. Contact us if you’d like to learn more about this.

No, the capstone project is only for participants who participate in the Real Estate Data Science module. But, the project can involve or be based on Geographic Information Systems techniques as well.

The default is individual capstone projects. However, if you are signing up for the course as a group, we’d welcome a group capstone project. If you do make contact with other course participants and decide to form a group to present a capstone project, that is acceptable - but please bear in mind that participants are from all over the world, and it will be your personal responsibility to coordinate the meetings, discussions, and work on the project.

Project presentations are individually scheduled typically 6 weeks after the last class. Participants are encouraged to begin thinking about and beginning preliminary work on the project as soon as possible. However, there is some flexibility to extend the project presentation date as needed.

Yes, the course includes two 20-minute one-on-one sessions with the instructors to assist with the project, and can optionally book one-on-one time with our TAs for additional help (at a nominal fee) if needed.

Yes. If necessary, you can mark your project presentation as “confidential”, in which case only the instructors will attend. Otherwise, we’ll invite other course participants to view and learn from your project presentation.

In session [1] of the course, we'll discuss public data sets and data vendors for a range of geographies. It's a seminar-like session (if you attend live), and participants do share what data sources they use. We also teach web harvesting techniques, so that you can assemble your own datasets to work on from a range of different types of online sources (e.g., government data sites, listing websites, map providers, pdf files, etc.). Also, you can discuss data sources and datasets in the slack channel.

Here's a summary of some of the projects previous participants in our course have worked on:

• Using data and forecasting methods to pick US housing markets to invest in. Starting with residential price index data for a large number of geographies in the US, the participant (a PhD candidate at a University) applied various forecasting methods that we taught in the course to make machine learning forecasts of price growth.
• Identifying office markets in the US with the highest future rent growth - to find market-beating opportunities. This project was by someone working in a real estate investment fund, who took data on commercial leasing prices across the US, forecasted, scored, and ranked growth for the upcoming year, to guide the fund's investment / deal sourcing decisions.
• Finding profitable fix-and-flip opportunities in South America. An independent fix-and-flip investor in Brazil used our web harvesting techniques to build a dataset of property listings. He then built an automated valuation model to identify undervalued properties to invest in, matching certain criteria (size, condition, location, undervaluation, etc.)
• Determining fair prices to bid on or market properties in Singapore. A group of participants collected a large set of data on Singapore private residential properties, then applied our automated valuation methods to build an accurate predictive model that was able to estimate fair prices for to buy / sell specific properties, as well as find undervalued investments.
• Finding the best development sites / opportunities using Geographic Information Systems. Several participants used our GIS module to study land parcels or sites, understanding demographic and location factors to determine where to develop properties, and what location-based factors influenced property price growth in the past.

Yes! Participants who successfully complete the course will receive a softcopy PDF certificate recognizing their accomplishment.

This is a Masters-level course in terms of content covered, and the instructors originally taught a version of the course at a leading global business school. However, note that we are not an accredited educational institution and do not confer degrees or diplomas.

Our course is not a licensing course. The course is not meant to prepare participants to be real estate agents, brokers, or realtors. Instead, it teaches the domain specific applications of data science to real estate.

Yes! All participants are invited to our community chat board where they can interact with alumni and current participants and also take advantage of careers and recruiting support through our partnership with LMRE. You’ll also be able to network with other participants in the TA sessions, live classes, and project presentations (where these are not marked “confidential”). We’ve seen participants form meaningful connections in previous batches, and encourage you to take advantage of this opportunity to connect with our niche community of individuals interested in the rapidly growing field of real estate data science.

This will be discussed in greater detail in Session 1 of the Real Estate Data Science module. The field is rapidly growing, and we see an increasing number of real estate companies hiring staff into data science roles (a number of whom are alumni in our courses). We also see increased emphasis on Data and Digital transformation, requiring suitably skilled staff. Do see the KPMG Proptech surveys 2018 and 2019, which highlight the emphasis on AI, Big Data, and Data Analysis as the technologies likely to have the biggest impact on the real estate industry in the long term, but also the skills gap in the industry - individuals with data science knowledge are in high demand in real estate!

Also have a look at this report from the UK Geospatial Commission highlighting the opportunities for those with programming, data science, and geospatial knowledge (look at “4. Address the skills and resource gap”).

Our partnership with LMRE for careers and recruiting underscores the demand from real estate and proptech companies.

We’ve partnered with LMRE., the market leading global proptech recruitment platform, to provide career support. Participants in our courses will be able to contact the LMRE team via our community chat board, and from time to time may hear about relevant opportunities globally.

There are a number of different delivery methods (live classes / video attendance / self directed), and the most relevant bundle of classes varies based on each participant’s background and learning goals. Please contact us to find out more, and we’ll be happy to share pricing information once we work out what might be most suitable for you.

We accept payment via credit card on stripe.com, bank transfer, or Paypal. The course is priced in USD, but we are able to accept payment in a number of other currencies as well.

Singapore participants: this course is eligible for NTUC UTAP funding and payable using SkillsFuture Credits!

UK participants: You can Pay with Knoma!

Yes. For courses starting more than 1 month in the future, we only require a USD 100 deposit to reserve a spot in the course. In general, payments can be divided into up to 3 installments. (The exception is “self directed” attendance, in which materials are shared all at once, and thus no installment payment options are possible.)

UK participants: Pay with Knoma will allow you to spread the course fee payments over up to 12 months!

UK participants aged 18+ can Pay with Knoma

Singapore participants can use SkillsFuture credits and get NTUC UTAP funding for this course.

There are no other financial aid programs currently available. If you believe that a government or other assistance program or tax relief program may apply, we are happy to supply relevant documentation to support your claim, but cannot guarantee that our course will be eligible for any particular grant, scheme, or program.

UTAP is a training benefit for NTUC members to defray their cost of training. Participants who sign up for an upskilling course with PropertyQuants can utilize the support provided.

Steps to Apply:

Step 1: Click on 'Search Course' under the "Skills Upgrade Available" tab to find out if the course and training provider is supported under UTAP.

Step 2: Register for course with training provider and attend training. For course information and enrolment, please contact the training provider.

Step 3: Login to the U Portal account to submit the UTAP application. NTUC Members should apply for their UTAP claim within 6 months after course ends. Late applications

NTUC members enjoy 50% *unfunded course fee support for up to $250 each year when you sign up for courses supported under UTAP. NTUC members aged 40 and above can enjoy higher funding support up to $500 per individual each year, capped at 50% of unfunded course fees, for courses attended between 1 July 2020 to 31 December 2022.

Note: This grant will be eligible from late-March 2022 onwards.

Learn more here.

All NTUC members can apply for UTAP. However, the following criteria must be met to be eligible for UTAP:

• Paid-up NTUC membership before course commenced, throughout whole course duration and at the point of claim
• Course by training provider must be supported under UTAP, and training must commence within the supported period
• Course must not be fully funded through company sponsorship or other types of funding
• You must achieve a minimum of 75% attendance for each application and sat for all prescribed examination(s), if any
• UTAP application must be submitted within 6 months after course completion

You can make a UTAP Application here.

YES! You can use NTUC UTAP funding and also Skillsfuture credits, substantially reducing your cash outlay for the course.

Pay with Knoma allows UK residents aged 18+ to spread the course fee over 12 months, with 0% interest and $0 fees.