Presentation: Liquidity Modeling in Real Estate Using Survival Analysis

Track: AI Meets the Physical World

Location: Cyril Magnin II

Duration: 1:10pm - 2:00pm

Day of week: Tuesday

Share this on:

Abstract

Hazard / survival modeling is often under-applied given its broad use cases. For example, churn prediction is often posed as a classification problem (did churn or not), when the time component is often given short shrift (when, if ever, did the churn happen?).

We hope to argue that hazard modeling is a better fit for these types of problems; spread general awareness of survival modeling, metrics, and data censoring; and describe how Opendoor uses these models to estimate our holding times for homes and mitigate risk, detailing scalability and other technical challenges we had to overcome.

Speaker: Xinlu Huang

Data Scientist @Opendoor

Xinlu is a data scientists on the pricing and revenue optimization group at Opendoor in San Francisco. The team focuses on developing per-home liquidity estimation and building responsive pricing models.

Before joining Opendoor, Xinlu was a theoretical particle physicist at Stanford. She holds a Master of Music in piano performance from Peabody Conservatory and PhD in physics from Stanford.

Find Xinlu Huang at

Speaker: David Lundgren

Data Scientist @Opendoor

David is a data scientist on the pricing and revenue optimization group at Opendoor in San Francisco. The team focuses on developing per-home liquidity estimation and building responsive pricing models.

Prior to joining Opendoor, David was a data scientist at Rdio working on music recommenders. He holds a BS in computer science from Binghamton University, and an MS in computer science from the University of Illinois.

Find David Lundgren at

Tracks

  • Deep Learning Applications & Practices

    Deep learning lessons using tooling such as Tensorflow & PyTorch, across domains like large-scale cloud-native apps and fintech, and tacking concerns around interpretability of ML models.

  • Predictive Data Pipelines & Architectures

    Best practices for building real-world data pipelines doing interesting things like predictions, recommender systems, fraud prevention, ranking systems, and more.

  • ML in Action

    Applied track demonstrating how to train, score, and handle common machine learning use cases, including heavy concentration in the space of security and fraud

  • Real-world Data Engineering

    Showcasing DataEng tech and highlighting the strengths of each in real-world applications.