The Amsterdam Business School (ABS) has a vacancy for a PhD candidate in Automated Valuation Models for Real Estate.

In many applications, property valuation plays an important role: local government need periodic valuations for tax purposes, Main Financial Institutions must determine the collateral value behind a specific mortgage to price the risk of default, and (institutional) investors need property valuations to set reservation prices when acquiring and selling properties, and to track the performance of their portfolio.

The basis valuation method is the direct comparison method; the assessed value is based on transaction prices of `comparable’ properties, where prices need to be adjusted for differences in characteristics between the subject and comparable properties.

Property prices depend on market conditions, location and property characteristics. Advanced econometrical models including temporal, spatial and cross-sectional components can help us greatly with pricing these shadow prices and mass value properties.

Automated Valuation Models (AVMs) are widely used to determine the market value of owner-occupied housing based on the aforementioned models. Examples are Collateral Analytics and Zillow in the US and Ortec Finance in the Netherlands.

However, for commercial real estate (successful) AVMs are not available, not in industry and not in academia. There is a number of reasons for this lack. The first is that the number of commercial properties is relatively low, and so is the number of transactions. Whereas the US has more than a 100M houses, which resale on average every 8 years, there are only a few 100 thousand commercial properties. The second is that the commercial property characteristics are not centrally collected, in contrast to housing where reliable and complete administrative data is typically available. In commercial real estate, data collection depends on private companies, which aggregate their data from multiple sources (mostly brokers and real estate newspapers). Data is therefore incomplete and more affected by entry errors. Data on maintenance, overall `architectural feel’ and structure quality are famous examples of data that is usually not observed in commercial real estate data at all. Finally, commercial properties are more heterogeneous compared to housing. In combination with the lack of collected characteristics this is a challenging combination. As a result econometrical models tend to have a low fit and out-of-sample performance.

We seek a highly motivated student with:

• an MSc degree in Computer Science, Artificial Intelligence, Mathematics, Business Analytics, or a closely related field;
• a sound theoretical background in mathematics, data mining, and machine learning;
• strong programming skills in Matlab, R or Python.

The appointment will be for a period of 4 years, with an intermediate evaluation after 18 months, resulting in a PhD thesis. An educational plan will be drafted that includes attendance of courses and (international) conferences. The PhD candidate is also expected to assist in undergraduate teaching.

The gross monthly salary will range from €2,222 in the first year to €2,840 in the last year. The Collective Labour Agreement (cao) for Dutch Universities is applicable.

Preferred starting date is 1 January 2018.

What do we offer you?

Some of the things we have to offer:

• a unique community of Data Science researchers;
• a friendly and informal working environment;
• a high-level of interaction;
• a location in the city centre;
• an international environment (10+ nationalities in the group);
• access to high-end computing facilities (cluster with CPU 4,000+ cores and 50+ GPUs).

Since Amsterdam is a very international city where almost everybody speaks and understands English.

University of Amsterdam

With over 5,000 employees, 30,000 students and a budget of more than 600 million euros, the University of Amsterdam (UvA) is an intellectual hub within the Netherlands. Teaching and research at the UvA are conducted within seven faculties: Humanities, Social and Behavioural Sciences, Economics and Business, Law, Science, Medicine and Dentistry. Housed on four city campuses in or near the heart of Amsterdam, where disciplines come together and interact, the faculties have close links with thousands of researchers and hundreds of institutions at home and abroad.   The UvA’s students and employees are independent thinkers, competent rebels who dare to question dogmas and aren’t satisfied with easy answers and standard solutions. To work at the UvA is to work in an independent, creative, innovative and international climate characterised by an open atmosphere and a genuine engagement with the city of Amsterdam and society.

http://www.uva.nl/en/home

Amsterdam Business School

The Amsterdam Business School (ABS) is a partner of Amsterdam Data Science, a network consisting of the academic knowledge institutes in the Amsterdam Metropolitan Area, and worldwide industry partners that focus on stimulating research and education in Data Science. The ABS is part of the Faculty of Economics and Business (FEB).The FEB provides academic programmes for more than 5,500 students and employs about 400 people. The Faculty conducts research in many specialist areas and participates in the Tinbergen Institute, one of Europe's leading graduate schools in economics, finance and econometrics.

http://abs.uva.nl/