租金 预测

by Zhen Liu

刘震

如何预测租金并优化租赁期限，从而节省资金 (How to Predict Rent and Optimize Your Lease Duration So You Can Save Money)

In my last post, we talked about how to pick the best month to sign the lease based on seasonality. Now, how long should you sign the lease for when facing different options like 12-month, 15-month, 18-month or longer? Is there any strategy in selecting the best option to save money?

在我的上一篇文章中 ，我们讨论了如何根据季节选择最佳月份来签署租约。 现在，当面对不同的选择(例如12个月，15个月，18个月或更长时间)时，您应该签署多长时间的租约？ 选择省钱的最佳选择是否有任何策略？

To analyze this, I modelled 353 cities’ rent data from Zillow (one-bedroom, city-level data). In this article, I will show you how to make time series predictions, and which cities are predicted to increase the most in rent!

为了对此进行分析，我从Zillow建模了353个城市的租金数据(一居室，城市级别的数据)。 在本文中，我将向您展示如何进行时间序列预测，以及预测哪些城市的租金涨幅最大！

首先，租赁期限如何帮助您节省资金？ (First, how does lease duration help you save money?)

As shown below, you can save money by signing a longer lease if you predict the rent will increase in your city. If the monthly rent increases $100 in the next year, you’ll save $1,200 by signing a 2-year lease, then renew it year-by-year.

如下图所示，如果您预计城市租金会上涨，则可以通过签一份更长的租约来省钱。 如果明年的月租金增加100美元，则您可以通过签订2年的租约，然后逐年续约来节省1200美元。

您如何预测租金会增加吗？ (How do you predict if rent will increase?)

We observed that rent is an additive time series with a combination of seasonality, trend and some random noise.

我们观察到，租金是具有季节性，趋势和一些随机噪声的组合的附加时间序列。

Additive model: Y(t) =Seasonality(t) + Trend(t) + Randomness(t)

加性模型：Y(t)=季节性(t)+趋势(t)+随机性(t)

We can decompose a time series into the right hand side of the equation above by applying R’s stl() function (stl stands for "seasonal and trend decomposition using locally weighted scatterplot smoothing”).

我们可以通过应用R的stl()函数将时间序列分解为上述方程式的右侧(stl代表“使用局部加权散点图平滑的季节和趋势分解”)。

# Decompose the additive time seriesdecomposed_rent <- stl(rent.series, s.window="periodic") #periodic means the seasonality factor is same for every year

# Extract the components from time seriesseasonal   <- decomposed_rent$time.series[,1]trend       <- decomposed_rent$time.series[,2]random     <- decomposed_rent$time.series[,3]

You can simply apply the st() function in R on the time series format of rent data to predict rent in the next 2 years.

您可以在租金数据的时间序列格式中简单地在R中应用st()函数来预测未来2年的租金。

# Forecast rent for the next 24 months with 95% Confidence Intervalfore_rent<-stlf(rent.series, s.window="period",h=24, level = 95)

预计哪些城市租金上涨？ (Which cities have the predicted increase of rent?)

*How to read the plots: The light green band area after 2018 is the 95% Confidence Interval of the rent prediction. The text in purple tells you how much you can save if you sign a 2-year rent vs 1-year rent, according to the purple rectangular area outlined. I used ggplot2 for all the plots.

*如何阅读地块：2018年后的浅绿 色带区域是租金预测的95％置信区间。 根据概述的紫色矩形区域，紫色文本告诉您如果签定2年租金与1年租金，您可以节省多少。 我将 ggplot2 用于所有绘图。

1.海湾地区 (1. Bay Area)

Sunnyvale’s predicted monthly rent increase is the greatest among all 246 cities I analyzed, which is $165 (comparing 2018–01’s rent to the predicted rent in 2019–01). So signing a 2-year lease in 2018 Jan can save you 165*12= $ 1980 on the second year; signing a 18-month lease can save 165*6 = $990. Given the seasonality effect in Sunnyvale, you should also try to avoid renewing the lease around July.

在我分析的所有246个城市中，森尼韦尔的预计每月租金涨幅最大，为165美元(将2018-01年度的租金与2019-01年度的预计租金进行比较)。 因此，在2018年1月签订为期2年的租赁，第二年可为您节省165 * 12 = $ 1980 ; 签订18个月的租约可节省165 * 6 = 990美元 。 考虑到森尼韦尔的季节性影响，您还应尽量避免在7月前后续订租约。

2，丹佛 (2.Denver)

3，南加州 (3.Southern California)

4.西雅图地区 (4. Seattle Area)

5.佛罗里达 (5. Florida)

6.德州 (6. Texas)

For the 11 cities above, if a 2-year lease isn’t an option, 18-months can still save a lot compared to an yearly updated increasing rent.

对于上述11个城市，如果不选择2年租约，则与每年更新的租金增长相比，18个月仍可以节省很多钱。

Which other cities show a huge leap in rent? I plotted the 20 cities total (including the cities mentioned above) to show you a comparison of rent as well as the increase of rent among more cities.

还有哪些城市在租金上有巨大飞跃？ 我绘制了20个城市(包括上述城市)的总数，以向您显示租金的比较以及更多城市之间的租金增长。

The length of line segment of each city is the increase of the rent where the red dot is the rent in 2018–01 and the green is the predicted rent in 2019 -01.

每个城市线段的长度是租金的增加量，其中红点是2018-01的租金，绿色是2019-01的预计租金。

From the plot above, Lakewood (Denver Metro in CO) and El Cajon (San Diego Metro in CA)’s rents are not that high among the 20 cities, but the “step” of increase is bigger compared to other cities with similar range of rent.

从上图可以看出，在20个城市中，莱克伍德(科罗拉多州的丹佛地铁)和埃尔卡洪(加利福尼亚州的圣地亚哥地铁)的租金并不高，但涨幅的“步伐”比其他类似范围的城市大租金。

The cities with rent >$2000 and significant predicted increase are all in CA (Top 4 of the plot). The rent there is already expensive, and they are getting more expensive, faster.

租金超过$ 2000美元且预计大幅增长的城市都在CA(地块的前4位)。 那里的租金已经很昂贵了，而且变得越来越昂贵，越来越快。

Among the top 20, there are 8 in CA, 6 in FL, 2 in WA, 2 in TX, 1 in NY and 1 in CO.

在前20名中，CA排名第8位，FL排名第6位，WA排名第2位，TX排名第2位，NY排名第1位，CO排名第1位。

有没有哪个城市的租金趋势不大？ (Are there any cities that don’t show much trend in rent?)

For the cities above, there’s no predicted increase. So for cities with very significant seasonality effect like Boston and Wilmington, it doesn’t really matter how long you sign the lease; but which month you sign.

对于以上城市，没有预计的增长。 因此，对于像波士顿和威尔明顿这样的季节性影响非常大的城市，签下租约多长时间并不重要。 但您签署的月份 。

The month with the highest rent in Boston is November, while it’s April in Wilmington.

波士顿租金最高的月份是11月，威尔明顿的月份是4月。

If you are curious about what are other cities like this, read more about cities with seasonality in my last post!

如果您对这样的其他城市感到好奇，请在我的上一篇文章中阅读有关季节性城市的更多信息 ！

Find the R code for time series models and visualization with ggplot2 here.

在此处找到用于ggplot2的时间序列模型和可视化的R代码。

Give me a few claps and follow me here if you find it helpful!

给我一些鼓掌 如果对您有用，请在这里关注我！

翻译自: https://www.freecodecamp.org/news/https-medium-freecodecamp-org-how-to-predict-rent-and-select-the-best-lease-duration-to-save-money-5cf35145d398/

租金 预测