Introduction

Formally, I’m trained in financial economics; the rigorous and robust estimation of expected risk and return. Real estate finance is my core discipline for research and education. When my work is applied in the interdisciplinary context of real estate development, in collaboration with my students and colleagues trained in design, planning and technology, we are tasked with creating value for 21st century real estate. For some, finance appears to be complex formulas, algorithms and a new language. Yet, when we examine the essence of the finance discipline, it is about decoding and sharing a compelling vision of future expectations based on past experiences. In this way, I argue that finance is a sister discipline to design and intimately linked.

Designers must understand the behavior of people, places, and things to synthesize and create malleable, yet durable, places or products for the human experience. The designer too must meet robust standards in the search for a compelling vision of the future: from ever more complex regulatory codes and building technology, design peer review and the interaction with progressive real estate developers. At the end of the day, financial and design perspectives must be aligned before any vision – or project – can be realized in the physical world. Without this alignment there is no path forward in our current economic system, for emerging technologies, buildings, products and processes that can advance the quality of our cities, and the sustainability of the planet.

My PhD and time at MIT have been focused on a central mission: to better understand — through the application of financial data science — how design and technology can be used as a strategy to achieve two complementary outcomes. To first improve the environmental, social experience and transparent performance of real estate; and second, the financial performance that benefits stakeholders at the community finance and institutional capital market scales. I execute this vision through the MIT Real Estate Innovation Lab; a research and development laboratory for the built environment that I founded almost six years ago and direct within MIT. I have been privileged to work with and financially aid over 50 graduate students and faculty members. This is a team of computational designers, real estate developers, financial economists, statisticians, and technologists who through the lens of financial data science observe events, experiences, people, and prices in real estate. We develop applications that count, correlate, and predict the value outcomes of design and technology strategies that are doing well by doing good.

For example, in New York City, when a project team provides natural daylight and view potential within office spaces, they can forecast an additional $6.27 in effective rent per square foot to their proforma (ceteris paribus). With the presence of street-level greenery, wide sidewalks and parks, they can add an estimated $7.26 in effective rent per square foot (ceteris paribus). Should they wish to go further and apply a healthy building certification like Well or Fitwell, they may attract some 4.4 to 6.8 percent more in effective rents per square foot (ceteris paribus). This way of studying and applying the learnings of data and design is a data-driven intention of the maxim ‘doing well, by doing good’.

My conceptualization of buildings and cities has moved beyond the economic concept of an asset class. Buildings are the locus of technological progress and human experience. They are simultaneously living and breathing incubators of social change and innovation estimated at a global asset value of $180 trillion, and therefore a systemic factor in contributing to, and resolving earth’s most pressing issues: climate change, inequality, resource depletion, and technological disruption. Meanwhile, I have also witnessed that in practice, real estate development is normative, emotional, and biased. The nuance and intricacy of human understanding and relationships do not always adhere to the human rationality and efficient information assumptions my financial models assume. Because of this, I am attentive to the narrative and messaging required to bring attention, industry critique and academic peer review to my findings. The work, and art, of the scientist-educator requires robust hypothesis testing, but also empathy, listening and story-telling, to contribute to the digitization of real estate practice.

My vision for the future of design and development curriculums and the practice of real estate development itself is to help these practitioners achieve the financial efficiency and value creation they seek. My intellectual contribution to design and development is to empirically assess where financial and economic incentives can assess and align outcomes to produce a better built environment. As a result, my research, organizational leadership values and passions seek to align with the process of design and development from multiple approaches – the resource calibration of real estate finance, the identification of the use of innovation and technology and empowering the story telling capacity of data science and machine learning. The remainder of this statement highlights my educational approach, research outcomes and vision for a design and development program centered on three areas: data science and machine learning, technology and innovation and real estate finance and economics. I conclude with summarizing my most important role as an academic – taking care of people who will serve as the agents of change to a better built environment.
Data Science and Machine Learning

When I teach data science and machine learning to students and executives for the built environment, I remind them that data is about us. The outcomes and features that populate a dataset are the collections of events that we have experienced collectively in the built environment. At the heart of the application of data science and statistics is the minimization of bias. The core assumptions that the scientist makes, when estimating from observational data, is that they did everything they could to estimate from a dataset with as little bias as possible. In fact, it is at the heart of the words that describe and premise a “best, linear, unbiased estimator.” Yet, in the practice of design, planning and finance, projects are often supported with the evidence of one or two so-called precedents or comparables. As a data scientist, I already know that the use of such limited experiences to support projections and forecasts is already biased.

To aid in this challenge, I have built and created the “Wide Data Project”; a geometric, geospatial and relational data integration project that follows every building and parcel in New York City over the 2000 to 2020 period. I integrate public, private and lab-curated data to follow the built environment and its outcomes over this time period. I have amassed 3000+ features for every building, with particular attention towards understanding financial, planning and design outcomes. This took cultivating at least 200 databases from 20 private data partnerships; and creating over 25 datasets in the lab for design, planning and entrepreneurship when they did not exist. And of course working with computational architects, urban designers, and city planners to create metrics and features that are embedded in the spatial experience of the built environment.
Further, I have then used this prototype as the foundation for both research and teaching my core focuses; finance and economics, technology and innovation, and data science and machine learning. This data infrastructure supports the topics and methods that are relevant to align stakeholders at the decision-making table. Our aim is to minimize bias in their decision making. To this end, I have curated three data science courses targeting various professional and practical aptitudes, that teach data science for real estate. By documenting the technical paradigms, dissecting and breaking down data science concepts, and creating guidebooks and kits, these courses enable data scientists and leaders alike to communicate ideas using a shared vocabulary. I believe we are just at the beginning of the data science journey. We will need to push harder and faster to cultivate a student and practitioner body at all levels that will be comfortable listening, ethically observing, and communicating with larger and wider datasets of the human experience.

Technology and Innovation

Importantly, when I discuss technology and innovation, I must clarify my point of view and training, stretching back to my PhD dissertation: Innovation in Commercial Real Estate. In this early publication, I laid the groundwork for how I would systematically apply technology studies and organizational strategy research to real estate finance. For me, technology is the application of products, processes, places, and organizational systems to make human life better. In contrast, innovation is the process of bringing those technologies to market through a marketable commercial product. This fundamental distinction has been at the core of understanding technological applications, commercial adoption, and market uptake of technologies in the built environment. This multi-year effort has resulted in the development of datasets and the assessment of outcomes across three realms: technologies for real estate; firms that bring those technologies to market; and educational strategies that we can incubate to help more firms cross the chasm from technology to commercial product.

The evolution of research within our technology and innovation track began with studying the technologies and design strategies necessary to develop green and healthy buildings. To answer this question with rigor, we developed the MIT Technology Tracker (The Tracker) in collaboration with JLL — a global commercial real estate broker. The Tracker is a tool that scouts, gathers, analyzes, and predicts the path of technologies for real estate with a big and wide dataset of technologies.

Although many technologies sound exciting, it is more important to go beyond the surface and examine how they can be applied and developed within a real estate practice. For example, a pension fund should learn about a technology like “hypercells”; a swarm of micro-robots that can travel to form complex structures remotely, but should invest in a commercially available technology like “literal green hydraulic walls” to improve the health and sustainability in their buildings. In contrast, SpaceX or Blue Origin should consider “hypercells” as potential systems for robotic and computationally intelligent construction on Mars. Our data and algorithms have enabled us to answer these questions for the real estate industry and contribute to the technology studies discipline.
Building off the framework developed within this tool, I have developed multiple courses, workshops and educational experiences to help students and practitioners with these concepts. Principally, I designed two graduate courses to explore these topics with real estate development students: Innovative Products, Spaces, and Technologies for Real Estate, and Technological Change and Innovation for the Built Environment. For these courses, I work on mapping the technology and innovation ecosystem, highlight real examples where technologies have moved towards innovation and commercial product offerings, and identify strategies for adopting innovation into the built environment using data, not gut. Further, I have developed with the REIL team, a Lifecycle of Technology educational experience, that helps executives learn about technologies that are relevant to them. This educational experience has led to our industry-directed white paper series, Automation Impacts for Real Estate.

Finally, what is most critical is to help our students. When I listen to students, they are seeking to invent. They create technologies with an aim to move them to market and so become entrepreneurs. Yet they are met with many obstacles. The process of innovation is complex, and my job is to help students navigate the multitude of demands as they move from inventors to innovators. As the co-founder and Head of Research for DesignX — a venture accelerator for student entrepreneurial ventures at the MIT School of Architecture and Planning — my responsibility is to create a meaningful accelerator program for students, and cultivate rigorous curricula on the role of technology and finance that are necessary for them to achieve their goals. To fulfill this objective, we developed a research project where we collected data on every accelerator program in the US, assessed the programs and amenities for each program, and assessed the financial incentive structure designed to help the firms. This resulted in my co-authored working paper research: Is Innovation in a Place? Assessing the Impact of Accelerator Programs on the Financial Performance of Entrepreneurial Firms. Results from this research helped guide the incentive structure for the DesignX program, space planning, and elements of the financial curriculum. Moreover, it helped us to understand that accelerator programs that paid attention to spatial syntax, financial incentives, and community development ultimately led to more venture funding firms downstream than those firms in nearby communities that did not go through an accelerator program.

Real Estate Finance and Economics

Last, but not least, is the core of what I do — the application and education of finance for design and development. Innovation in commercial real estate has been the foundation of my research and teaching. To innovate in finance means we need to understand the benchmark or standard, how a product will differentiate to meet the heterogeneous preferences of a community, and ultimately how that leads to pecuniary and non-pecuniary performance outcomes for stakeholders. This simple approach has guided my teaching of finance, real estate finance and entrepreneurial finance. In addition, how I identify and measure value.

My work in real estate finance has been principally in two core areas: benchmarking the financial performance of commercial real estate and valuing commercial properties by understanding the design and technology strategies that lead to product differentiation. To benchmark an asset’s performance is to understand its measures of central tendency and how this can become distorted by various product differentiations and idiosyncratic noise. In this way, my approach to curating benchmarks — so-called price indices — is to develop econometric techniques that I feel approach systemic bias in real estate in a more formal way. My formative work in real estate finance was in developing commercial property price indices through various techniques and understanding the correlations across those econometric procedures.

Lastly, my approach in teaching real estate finance is deeply embedded in the rigor of the groundwork laid out in Geltner, Miller, Clayton and Eichholtz, 2018. I have learned and read this textbook, along with others in my teaching of Real Estate Finance and Economics alongside Professor dr. Piet Eichholtz and curating primer courses for Professor Geltner. In these situations I spent a number of years teaching finance principles to highly trained business students, but also spent the last eight years teaching architects and planners in the Real Estate Innovation Lab, and alongside Professor Dennis Frenchman in Real Estate Development Design Studio and DesignX. My contribution in these educational settings is the deconstruction of financial vocabulary to work towards a visual mapping of financial concepts. Although this was at first a very challenging and confronting exercise, I myself enjoyed learning from my students how they paired their design approach with explaining finance. So much so, that my students and REIL team co-authors have a book contract with Oro Publishers that focuses on “The Value of Design, Design Agency in a Market Economy” as a tool for designers to gain more insight into the interaction of design, planning and finance in city-making.

Looking Forward

Finally, I end my statement on my most important role in education; to take care of people. We can push to the technological frontier, estimate increasingly complex models, expand design to the edge of human experience, amass more data, and even construct systemically relevant and leading buildings and communities. However, this is nothing, if our people are excluded and cannot thrive and grow. I’ve now worked for eight years in a high-performing academic institution, led a lab through six of them, and through the tumult of “Me too”, “Black Lives Matter”, “Stop Asian Hate”, “Pride”, “January Insurrection”, the greatest period of income inequality in our measurable history, and a deadly global pandemic. I have learned that my largest contribution as a leader, educator and colleague is to put down my pen, close my laptop, and to listen to my students and research team when they are scared and uncertain about how change will impact them. To guide them through the storm and reinforce their resilience, despite these challenges and in some cases, even use them to create opportunities. Because in the long-run, these people are the true agents of a better built environment.