A Beta Look: Mathematical Framework to Buying a Home

Recently, my friend was interested in purchasing a home in Palo Alto. However, she was unsure what the offer price should be given the variance and competition. Out of curiosity, I decided to figure out what would be a fair offer price. Using data from Redfin, I did an analysis of the current market trend and recommended a bid price strategy for her.

I wanted to understand how the market is currently behaving. Since the home is close to the borderline between Palo Alto and Los Altos Hills, I gathered data from 2012 – 2016 for the larger Palo Alto and smaller Los Altos Hills homes. I looked at metrics such as average sale to list price percentage, percentage of homes sold above asking price, and the median days on market.

After analyzing the data, I concluded that competitiveness for homes have dropped since January 2016. The sales to list price percentage and percentage of homes sold above asking price have both decreased while the median days on market have increased. This indicates that the the housing market is starting to cool off and we should expect bids to be less competitive. To estimate a fair offer price for the home, I looked at where this home lies compared to others in regards to the average sale to list price.

The methodology includes creating a sale to list percentage hotness gradient, using Redfin’s views and favorites to approximate “hotness”, and calculating a sale to list percentage for this home. I looked at homes sold in the past three months in Palo Alto, Los Altos, and Los Altos Hills. I concluded that a very hot home is sold at 116% above listing price while a cold one lies at 95%. To estimate the hotness of 4192 Manuela Ave, I used Redfin’s views and favorites for homes priced in a similar range to compare. Based on the comparison, this home is considered relatively hot so it is expected to sell between 106% ($2.85M) – 112% ($3.0M) above listing price.
Since the housing market is starting the weaken, I recommend that $2.85M is a fair offer price with room to negotiate up to $3.0M. Any price point above $3.0M should be avoided as it would be too expensive.


A Beta Look: How Understanding the App Store Can Help Your App Grow


After my last blog post on the mobile app ecosystem, I received a lot of positive feedback and had some very interesting discussions. I decided to do a followup analysis that digs deeper into the install behavior of the Apple App Store. Using install data for the last year, I looked at trends for the top 5 apps by installs and analyzed how the data fluctuated over time. Installs spiked during Christmas and Valentine’s Day as smartphones and tablets are often gifted during those holidays. School seasons also had a large impact particularly to social apps as students are a big part of their demographic. Aside from seasonality, the App Store is also more active on the weekend than on weekdays, which is likely due to people spending more time on their phones and tablets. What this all means is that when developers plan launches or promotions, they need to be aware of the install behavior on the App Store. Different strategies can be used to either target hot periods where there are a lot of eyeballs or to target days where the competition is less fierce.

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A Beta Look: Million Dollar Shack

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Silicon Valley has always been known as the home to tech behemoths that produce gadgets used worldwide. Recently, it has also produced million dollar shacks. Home prices in the valley have always been above average but in recent years, affordability is at an all time low with even more demand than the pre – 2007 Housing Bubble. There are two reasons behind this phenomenon. Tech IPOs have increased the purchasing power of many individuals, allowing them to afford homes at higher price points. Also, the increase in foreign investments from China have fueled the spike. However, with the number of tech IPOs decreasing and the Chinese economy slowing down, there will be a correction soon.

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$2M Shack



A Beta Look: Google Shopping Express


On my way to work, I saw a little van with a blue parachute painted on it. This sparked my interest in Google Shopping Express.

Google is breaking into e-commerce with Google Shopping Express. Google Shopping Express allows local retailers such as Target and Walgreens to sell their goods through Google’s website. After the user places an order on the website, Google will send their couriers to pick up items from the retailer and deliver them during the same day.

Amazon is almost synonomous with e-commerce. I wanted to find out how Google compares with the e-commerce behemoth so I did a business study by first finding the relationship between marketing spend and sales. Then using Amazon as a benchmark, I analyzed Google’s profit and losses if Google were to gain Amazon’s market shares in three different cases: 5%, 10%, and 15%.

The only scenario where Google is profitable is the optimistic case where it has 15% of Amazon’s market share. However, even if Google Shopping Express were unprofitable, it would still have positive externalities in terms of ad revenues. The shopping service would lead to an increase in the volume of product searches, which translates to greater product listing ad revenues. Through the service, the search giant can collect even more personal data that allows them to improve on how they target ads. Although Google Shopping Express seems to be Google’s attempt at competing with Amazon, it can have a significant impact to Google’s core search business.

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A Beta Look: Facebook Ads


I have always been curious as to how Facebook makes money from ads so I decided to run my own campaign for my puppy named Miko.

I created a page for Miko and my goal was to gain page likes with the lowest cost. I chose four different visuals with four distinct phrases for two pricing methods: optimized CPC and CPA. I allocated $5 a day for each of the pricing mode. After running the campaigns for a couple of days, I noticed a significant difference in terms of the metrics even when comparing between the two pricing methods and that Facebook channels a majority of the traffic to the most popular ad leaving the others with low impression, click-through, and conversion rates.

Due to the low CPA and high conversion rate for optimized CPA, I lowered the budget from optimized CPC so that most of the resources were invested in the former. Although the ads in optimized CPA had lower impression rates, it had high conversion rates and low CPA and CPC. This indicates that the quality of users are higher and thus, are more likely to like the page.

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A Beta Look: Online Dating


During dinner, my friends were discussing the popularity of all the online dating services. This piqued my curiosity about the difficulty of finding love. I decided to take a statistical look at the difficulty of finding a soul mate as well as ideas that can help with this age-old challenge.

I took a look at the chances of a 25 year old female professional finding true love. Assuming that she is only interested in men in the Bay Area along with several trait preferences, there are only 116 potential matches out of 7 million people, which is a mere .002%! To improve the odds, one idea is to leverage the existing personal data that is stored in different online services. For example, if someone enjoys watching Game of Thrones on Netflix, we can recommend potential matches that also like Game of Thrones. Although this can be a great way to solve this problem, privacy will definitely be of concern.

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